Monthly Archives: January 2025

Putting A Human Face on ‘Burnout’ in the World of Medicine: Using AI and Mind Genomics Thinking about Mind-Sets to Create a Sense of what is Going on, and What to Do

DOI: 10.31038/ALE.2024124

Abstract

This paper explores the emerging problem of burnout among medical professionals. The approach is a combination of simulations using artificial intelligence structured by Mind Genomics thinking about mind-sets. The paper begins with using the simulations to explore the way professionals talk when in a staff meeting at a local hospital when the hospital was independent versus when it was acquired by a private equity firm. The paper continues with the exploration of mind-sets about the way medical professionals burn out, showing five different patterns or types of people. The paper continues with the use of AI to suggest messaging for the five burnout mind-sets to encourage preventive actions, and then finishes with the use of AI as a co-development coach or even partner. Through the paper the stress is on simplicity, speed, iterations to gradually improve, and the opportunity for the democratization of solving problems and advancing medicine.

Introduction

The topic of burnout and the immediate stimulus for writing this paper comes from a web posting in the news section (Table 1). Service in the medical profession at all levels and in all functions seems to be hitting the ‘respected brick wall’, of frustration. Medical professional burnout is causing higher healthcare costs, higher turnover rates, and decreased patient care. This is detrimental to the healthcare system and patients. Non-medical professionals should address mental health and well-being, empathizing with healthcare workers experiencing burnout. They should advocate for better support systems and resources for medical professionals. Concern should also arise for non-medical professionals, as a healthcare system struggling to meet patient needs due to burnout could lead to dangerous situations and subpar care [1-4].

Table 1: The Story about burnout the world of burnouts in the world of medicine.

Phase 1 – Simulations of Interviews with People

Taking a simulation approach to understanding burnout in the medical profession (Table 2) allows for a more in-depth exploration of the personal thoughts and feelings of those experiencing it. By bringing together a variety of relevant individuals, such as doctors, nurses, and administrators, and simulating discussions using advanced AI like GPT 3.5, this method delves into the underlying issues driving burnout. It personalizes the experience by allowing participants to express their true feelings, creating a more intimate and detailed understanding of the challenges they face.

Table 2: Three simulations about the causes of burnout, two from medical professionals, one from employees who are not medical professionals.

The benefits of this approach include gaining insights that may not be uncovered through traditional methods like surveys or interviews. First, simulations allow the investigator to do more deeply into inner thoughts and motivations, leading to a deeper understanding of the root causes of burnout. Second, simulation of interviews allows for a more nuanced exploration of complex relationships and dynamics within the healthcare system, providing a holistic view of the issues at hand.

When people question the validity of using simulations and AI like GPT 3.5 for understanding burnout, it can be argued that this method allows for a unique glimpse into the minds of individuals in the medical profession. By focusing on personal experiences and emotions, rather than just facts and figures, simulations provide a rich tapestry of insights that can inform future interventions and solutions. The simulated interview offers a novel way to explore the complexities of burnout and relationships within the healthcare system, paving the way for more effective strategies to address these challenges [5-8].

Phase 2 – The Value of Mind-sets and How AI Uncovers These Mind-sets Through Simulation

Carol Dweck’s seminal work on mindsets developed the concept of fixed vs growth mindsets, emphasizing the value of trusting in one’s ability to learn and evolve. This concept has been used to many fields, including medicine. Mind Genomics, as well as Moskowitz and colleagues’ research, have expanded on the concept of mindsets in healthcare, utilizing experimental design to blend multiple messages about the medical experience and analyze how people respond to them [9-11].

Researchers were able to identify fundamentally diverse attitudes among individuals by employing well established approaches such as regression analysis, grouping, and permutation. These mindsets are not necessarily innate characteristics, but rather modes of thinking that can influence behavior and decision-making.

In Phase 2 we simulate mind-sets, first using mind-sets typically discussed in the popular press because it involves the world of the everyday. We then move to simulating mind-sets focusing strictly on patterns of burnout among medical processionals (Table 3).

Table 3: Simulation of burnout mind-sets.

Simulating Solutions to Problem by the Targeted Messages Appropriate for a Mind-set

One way to begin dealing with burnout is the ‘soft’ approach of messaging. The development of Mind Genomic starting almost 40 years ago in the mid 1980’s recognized that there were different mind-sets for items as simple as toothpaste. The continuing use of Mind Genomics, and the emerge of speed, the lowering of cost, and the ease of application ended with revealing that much could be gained by working with mind-sets to craft effective messages.

Table 4 shows how generative AI can be ‘fed’ a group of mind-sets, and emerge with appropriate messages that might be used. In conventional use, these messages might either be developed with empirical-based Mind Genomics using people, or at least checked and validated later before use. Right now, however, Table 4 shows a richness of messaging to jump start the solution and can probably outperform many suggestions emerging from brainstorming. The process can be looked at as a cost-effective AI-brainstorm.

Table 4: Five AI-suggested mind-sets of doctors based on burnout patterns, and AI-suggested appropriate messaging to them to reduce burnout.

Using AI as an Invention Machine, or at least as an Invention Colleague

Generative AI, along with Mind Genomics thinking, can transform the way we address burnout by proposing new ideas that provide general guidance and explain what the innovation achieves/ The strength of generative AI in conjunction with Mind Genomics thinking stems from its potential to handle complicated human nature concerns, such as burnout, which are currently baffling and difficult to address.

Using the Mind Genomics platform such as BimLeap.com, those in the medical world may run the AI numerous times in minutes and quickly modify components of their request to adapt the suggestions to their exact needs. This ability to swiftly produce and customize burnout solutions can be extremely beneficial when made publicly available, low-cost, and user-friendly.

Table 5 shows nine innovations from one run. The objective of showing the ideas in Table 5 is to demonstrate the ease with which AI can become an integral part, even perhaps a ‘member’ in the effort to solve human-experience problems, where the issue no long is ‘factual correctness’ but rather ‘it is useful?’.

Table 5: Nine suggested innovations suggested by AI and returned to the user automatically after the ‘study’ is closed and AI has had a chance to further analyze the information it provided. The material comes from the ‘Idea Book’, and Idea Coach, attached to the project.

Discussion and Conclusions

Generative AI coupled with Mind Genomics thinking has the potential to revolutionize the way we approach burnout by suggesting innovative solutions that give general direction and outline what the innovation accomplishes. By utilizing a Mind Genomics platform like Bimieap.com, users can run the AI multiple times in minutes and easily change aspects of their request to tailor the suggestions to their specific needs. This ability to quickly generate and customize solutions for burnout can be incredibly valuable when made widely available, inexpensive, and user-friendly.

The power of generative AI in conjunction with Mind Genomics thinking lies in its ability to tackle complex human nature issues, such as burnout, that are currently perplexing and challenging to address. With the AI’s capacity to generate a multitude of suggestions and ideas, users can explore a range of innovative solutions that may not have been considered before. This opens up new possibilities for individuals to find effective ways to combat burnout and improve their overall well-being.

The democratization of expertise through generative AI and Mind Genomics platforms like Bimieap.com means that everyone can become an expert when it comes to addressing personal challenges like burnout. By empowering individuals to take control of their own solutions and decisions, this technology promotes a sense of agency and ownership over one’s well-being. This shift towards self-directed problem-solving can lead to more effective and sustainable strategies for managing burnout.

As generative AI becomes more widely accessible and user-friendly, the potential for addressing a range of human nature issues beyond burnout increases exponentially. From stress management to mental health support, the applications of AI and Mind Genomics thinking are limitless. By encouraging widespread discussion and exploration of innovative solutions, this technology has the power to catalyze positive changes in how we approach and overcome challenges in our daily lives.

Overall, the combination of generative AI and Mind Genomics thinking represents a new frontier in problem-solving and innovation, offering individuals the tools and resources they need to address complex issues like burnout in a creative and effective manner. Through this technology, we can tap into our collective expertise and wisdom, empowering everyone to become a master of their own well-being. The future looks bright with these powerful tools at our disposal, ready to shape a more resilient and thriving society.

Acknowledgments

The authors thank Vanessa M Arcenas and Isabelle Porat of the Tactical Data Group for help in preparing this and companion manuscripts in this set.

References

  1. Bridgeman PJ, Bridgeman MB, Barone J (2018) Burnout syndrome among healthcare professionals. The Bulletin of the American Society of Hospital Pharmacists 75: 147-152. [crossref]
  2. Chou LP, Li CY, Hu SC (2014) Job stress and burnout in hospital employees: comparisons of different medical professions in a regional hospital in Taiwan. BMJ open 4: e004185. [crossref]
  3. Erschens R, Keifenheim KE, Herrmann-Werner A, Loda T, Schwille-Kiuntke J, et al. (2019) Professional burnout among medical students: systematic literature review and meta-analysis. Medical teacher 41: 172-183. [crossref]
  4. Schrijver I (2016) Pathology in the medical profession?: taking the pulse of physician wellness and burnout. Archives of pathology & laboratory medicine 140: 976-982. [crossref]
  5. Fuse Brown EC, Hall MA (2024) Private equity and the corporatization of health care. Stanford Law Review 76.
  6. Gerard RA (2024) Lead With Purpose: Reignite Passion and Engagement for Professionals in Crisis. Friesen Press.
  7. Howell TG, Mylod DE, Lee TH, Shanafelt T, Prissel P (2020) Physician Burnout, Resilience, and Patient Experience in a Community Practice: Correlations and the Central Role of Activation. J Patient Exp 7: 1491-1500. [crossref]
  8. Peterson MD (2024) Another View on Workforce Projections: A Bright Future, but the Details Can Be Cloudy. ASA Monitor 88: 29.
  9. Jahja E, Papajorgji P, Moskowitz H, Margioukla I, Nasto F, et al. (2024) Measuring the perceived wellbeing of hemodialysis patients: A Mind Genomics cartography. Plos one 19: e0302526. [crossref]
  10. Moskowitz Howard R, Stephen D Rappaport, Sunaina Saharan, Sharon Wingert, Tonya Anderson, et al. (2024) “Mind-Sets for Prescription Weight Loss Products That Are Advertised Directly to Consumers: Using Mind Genomics Thinking with AI for Synthesis and Exploration.” Acta Scientific Pharmaceutical Sciences (ISSN: 2581-5423)
  11. Moskowitz H, Rappaport SD, Wingert S, DiLorenzo A, Sunaina Saharan, et al. (2024) Learning from Ai Synthesis of Mind-Sets: Dealing with Patients Injured by Violence in The New York City Subway System. J Clin Nur Rep 3: 01-06.

Putting a Human Face on Legal Disputes: Eviction for Non-Payment of Rent Simulated by AI and Mind Genomics Thinking

DOI: 10.31038/ALE.2024123

Abstract

Using generative AI (Chat GPT3.5), the paper shows how a legal case such as eviction for non-payment of rent can be ‘brought to life’. The first simulation shows how AI can be used as a preparatory device for the Mind Genomics experiment, thus making the lawyer’s job easier when using Mind Genomics for a legal case. This first simulation focuses on AI-created questions and answers, and the ‘guesstimate’ by AI as to the likelihood that a jury will find ‘for the plaintiff’ vs ‘for the defendant’. The second simulation focuses on different reasons for the eviction, and AI-based analyses of legal aspects vs ethical aspects. This second stimulation demonstrates the ability of AI to consider the nuances of a case. In both simulations the focus is on types of issues that are best described by phrase ‘subjective, and open to interpretation.’

Introduction

As legal education evolves, law schools are placing greater emphasis on equipping students with the skills to critically analyze intricate legal cases. Traditional methods for teaching law methods frequently rely on rote memorization of statutes and procedures. However, a growing number of educators are now embracing experiential learning opportunities, allowing students to hone their analytical skills in practical contexts. In this new era of legal education, innovative technologies such as generative AI are increasingly being utilized to simulate legal cases, enabling students to navigate real-world scenarios effectively. When combined with the emerging science of Mind Genomics the new mix enables students, or indeed anyone, to gain a human perspective on the law, viz., put a human face on the facts through simulation. The simulation allows learning by asking ‘what-if’ questions, not about the case law itself, but about the softer, human aspects [1-5].

This paper shows two simulations of the same case, where a landlord seeks to evict tenants who have fallen behind on rent for two months due to job loss. The case itself is rich with legal versus emotional and moral complexities. In the classroom, students might end up relishing the back-and-forth analysis, as they engage in a spirited discussion, exploring the pertinent legal principles, the rights of both landlords and tenants, and possible solutions to the issue at hand. This exchange of ideas enables students to examine various viewpoints, sharpen their arguments, and gain a richer insight into the intricacies of legal decision-making.Can we make this back and forth into an AI application, using currently available LLM’s, large language models? As this paper shows, we can do so in ways which are staggeringly fast, inexpensive, scalable, and adaptable to many situations [6-8].

Simulation in the legal field breathes life into cases, offering a vivid experience which textbooks and lectures often fail to convey. Generative AI lets law students explore the intricate complexities and subtleties of real-world legal dilemmas through simulated cases. In this scenario, the AI offers clarity on landlord-tenant laws, the eviction process, and the delicate balance of interests between property owners and tenants.Students can delve into various viewpoints and possible results of the case through simulation. The legal rights and responsibilities of both landlords and tenants can be examined, alongside an evaluation of the ethical implications stemming from the decisions made by each party. This practical method sharpens critical thinking and fosters a richer comprehension of legal principles at work.

This paper explores the two simulations, based on the same ‘facts of the case’. Simulation 1 deals with what to present to a jury to get the jury to vote for the client. Simulation 2 moves more deeply into the role of the lawyer as an advocate for their client.

Simulation 1: Mr. Owner vs Mr. & Mrs. Tenant – Rent Dispute and the Search for Discovering Powerful Messages Resonating with the Jury

We begin with the facts in the case, shown in Table 1. The stimulation is set up to provide messages that can be tested by the emerging science of Mind Genomics, to identify those specific messages which will resonate with a jury [9-12].

Table 1: Facts of the case and instructions to AI (Simulation 1)

The actual Mind Genomics experiment with real people (not shown) follows a straightforward process. The steps are as follows:

  1. Select a topic
  2. Develop four different questions about that topic which give a human face to the topic
  3. For each question develop four different answers to the question, phrased as stand-alone phrases which paint a word picture
  4. Combine the 16 elements into small, easy-to-read vignettes comprising a minimum of two and a maximum of four elements. The composition of the 24 vignettes to be evaluated by a single respondent follows an underlying experimental design. Each question contributes only one element to the vignette. Across the set of 24 vignettes each of the 16 elements appears 5x and is absent 19x. Mathematically the structure of each respondent’s set of 24 vignettes is identical, but the permutation ensures that the vignettes are different across all respondents.
  5. Each respondent evaluates a permuted set of 24 vignettes, using a simple rating scale. The vignette is a stand-alone composition, easy to read. The respondent requires about 3-4 seconds to scan the combination of messages and assign a rating.
  6. The data are put into a simple data base to prepare them for statistical analysis. Ordinary least-squares, dummy variable regression analysis shows the driving power of each element, first for each respondent, and then for groups of respondents defined by methods such as cluster analysis.

Up to now the creation of raw material, questions especially, has emerged as a stumbling block. For whatever reason, people are intimidated. Over a 24-year period, 1998 to 2022, the same plaint was heard, always by the researcher using the tool. It was simply daunting, although children had fewer problems than did adults, and especially far fewer problems than did professionals. Professionals wanted perfection in the vignette, ending up enamored with overly polished, wordy paragraphs, hard to read, hard to test more than a few.

The introduction of AI in 2022 revolutionized this emerging science of Mind Genomics. As Table 1 showed it is straightforward to come up with the statement of the ‘facts of the case’. It is the creation of the questions and elements (answer) which are difficult.

Table 2 shows what emerged after one iteration requiring 10-15 seconds. These four answers are statements about the case, and the AI’s ‘guesstimate’ about the jury’s decision based on each answer. The key benefit to critical thinking is the ability of AI to jump-start the thought process. To ‘have fun’ one could iterate, with each iteration requiring the 15 seconds. An encyclopedia of questions and elements (answers) would emerge in about 10 minutes or shorter. The enterprising user could change the facts in the case as well and explore different ‘what if’ aspects, using the Idea Coach features.

Table 2: Simulation of four questions and four elements (answers to each question), prepared for a Mind Genomics experiment with actual people (Simulation 1).

The effort is finished, whether one iteration or 100 iterations, the results of each iteration are stored on an Excel spreadsheet, with one tab dedicated to each iteration. Thus, it is possible to run many iterations, acquire the data, but leave the subsequent analysis to an off-line effort, that effort taking about 5-6 hours. The results are returned to the user by email. The remainder of this simulation is generated after the study preparation is closed. AI then goes over its own questions and answers and provides a summarization and deeper analysis shown in Table 3, and beyond. Table 3 presents the first set of information,

  1. Key Ideas: AI can provide valuable insights into key ideas by analyzing large amounts of data and identifying patterns and trends. It can summarize complex concepts and present them in a clear and concise manner. This can help individuals better understand key concepts and make informed decisions.
  2. Themes: AI can also identify common themes across different sources of information, allowing for a deeper understanding of the subject matter. This can help in identifying trends and patterns that may not be apparent at first glance.
  3. Perspectives: AI can provide different perspectives on a topic by analyzing a wide range of sources and presenting diverse viewpoints. This can help individuals gain a more comprehensive understanding of the subject matter and consider different angles.
  4. What is missing: AI can also highlight what is missing in a particular discussion or research study by pointing out gaps in information or potential areas for further exploration. This can guide researchers in identifying areas that need further investigation.
  5. Alternative viewpoints: Additionally, AI can present alternative viewpoints on a topic by analyzing conflicting information and presenting different sides of the argument. This can help individuals consider different perspectives and make well-informed decisions.

Table 3: AI summarization of simulation 1 in terms of key ideas, themes, perspectives, what is missing and alternative viewpoints.

Table 4 compares interested audiences versus opposing audiences regarding the material presented in Table 3. AI can expand the topic by questions and answers tailored to specific interested audiences, allowing for a more personalized and engaging experience for learners. AI can create content that is relevant and meaningful content. increasing their motivation and interest in the material. Going a step further, AI can also identify prospective opposing audiences by presenting diverse perspectives and challenging viewpoints. By generating questions and answers that provoke critical thinking and debate, AI can stimulate discussions and encourage individuals to consider alternative viewpoints. This can lead to a more well-rounded understanding of the topic and foster a culture of open-mindedness and tolerance.

Table 4: AI synthesis of interested versus opposing audiences, based on material in Table 3.

The final analysis as of this writing (November 2024) is the suggestion of innovations. The initial suggestion of innovations produced a list of AI-generated innovations. During the summer of 2024, the Mind Genomics system was modified so that the post-creation of the study (not-yet-run with people) would offer a far more complete analysis of each innovation that was being created and offered to the user. These nine aspects appear below and are shown in Table 5 for each of the four innovations.

  1. Specific suggestion: AI can provide specific and targeted suggestions based on data analysis and patterns
  2. Explanation why the suggestion is relevant:
  3. Importance and uniqueness of the suggestion:
  4. Social Good resulting from the suggestion.
  5. Slogan which emblemizes the suggestion:
  6. Investment pitch:
  7. Potential investor pushback:
  8. Answer to the potential investor pushback:
  9. Compromise, go forward positioning:

Table 5: The four innovations suggested by AI and an AI ‘work-up’ of each innovation

Simulation 2 – Dealing with the Same Issue, but From the Point of View as the Lawyers Helping Both Sides as ‘Trusted Parties’

In this simulation we move backwards, away from the situation of a lawyer trying to anticipate what to say to the jury (Mind Genomics approach), to understanding the ‘facts in the case’ from the point of view of the client. Here the lawyer becomes a trusted friend. The process becomes more personal and empathic.

Table 6 shows the ‘facts of the case’ and the instructions to AI to generate the relevant questions and answers. Table 7 shows nine different questions and the structured answers to those questions.

Table 6: Facts of the case and instructions to AI (Simulation 2)

It is worth noting here that generative AI such as Chat GPT3.5 used here, can be instructed to simulate different people talking to each other, e.g., listening in as a ‘fly on the wall’ to the lawyer’s conference.

Table 7: Nine relevant questions generated by AI regarding the case, with answers simulated by AI with respect to from the legal standpoint and the moral standpoint.

Discussion and Conclusion

Simulations are crucial for students to understand the intricacies of legal decision-making, enabling active engagement with complex scenarios and practicing their knowledge in real-world contexts. These simulations enhance students’ understanding of the law and its real-world uses, as well as their critical thinking and problem-solving abilities. The future of law schools will likely employ diverse techniques and technologies, guiding students to think critically about complex issues. Mind Genomics, enhanced by AI, presents a groundbreaking solution for the legal system, offering students an engaging and interactive approach to grasp the intricacies of legal cases. This technology has the potential to reshape the legal landscape, fostering quicker case resolutions, enhancing transparency, and expanding access to justice for everyone. Mind Genomics and generative AI can craft realistic legal scenarios by examining the many factors which shape legal decision-making, including case law, evidence, and ethical considerations. These scenarios can provide students with practical experience to enhance their legal reasoning skills in a simulated environment.

Mind Genomics, powered by AI, has the potential to revolutionize the legal profession by improving decision-making, streamlining case resolutions, and ensuring justice is served efficiently and transparently. This technology would benefit students by providing interactive learning tools, real-world case studies, and hands-on experience, while professors would have access to cutting-edge research tools and real-time data. The implementation of Mind Genomics could transform the legal practice, leading to more efficient case resolutions, improved transparency, and greater access to justice for all individuals.

Reflection and debriefing sessions on simulated legal cases can help students improve their legal reasoning abilities, bridge the gap between theoretical legal knowledge and practical application in real-world scenarios, and develop a deeper understanding of human factors involved in legal decision-making. At the same, it might well be productive is during the application of AI to legal issues the user requests both legal and ethical/moral considerations as separable steps in the process.In conclusion, using technology-driven simulations in legal education can promote active learning, enhance critical thinking skills, and provide practical experience in legal decision-making, equipping students for successful careers in law.

Acknowledgments

Author Moskowitz wishes to acknowledge the help of the audience at his lecture at the law school in of the University of Kragujevac, November, 2024. Many of the points of view regarding ethics and morality and the law were challenged by the students and professors, leading to refinements in the authors’ thinking.

The authors would like to thank Vanessa M. Arcenas and Isabelle Porat for help in preparing this manuscript for publication.

References

  1. Curcio AA (2009) Assessing differently and using empirical studies to see if it makes a difference: Can law schools do it better. Quinnipiac L Rev 27: 899.
  2. Horwitz P (2012) What Ails the Law Schools. Mich L Rev 111: 955.
  3. Katz HN (2006) Evaluating the skills curriculum: Challenges and opportunities for law schools. Mercer L Rev 59: 909.
  4. Mertz E (2007) The language of law school: learning to” think like a lawyer”. Oxford University Press, USA.
  5. Shah M (2010) The legal education bubble: how law schools should respond to changes in the legal market. Geo J Legal Ethics, 23: 843.
  6. Alarie B, Niblett A, Yoon AH (2018) How artificial intelligence will affect the practice of law. University of Toronto Law Journal, 68: 106-124.
  7. Jinzhe Tan, Hannes Westermann, Karim Benyekhlef (2023)ChatGPT as an Artificial Lawyer? Canad Workshop on Artificial Intelligence for Access to Justice (AI4AJ 2023)
  8. Susskind R, Susskind RE (2023) Tomorrow’s lawyers: An introduction to your future. Oxford University Press.
  9. Moskowitz H, Kover A, Papajorgji P (2022) Applying mind genomics to social sciences. IGI Global.
  10. Moskowitz HR, Wren J,Papajorgji P (2020) Mind genomics and the law. Mauritius: LAP Lambert Academic Publishing.
  11. Papajorgji P (2023) Knowledge as a service: The case of Mind Genomics. EuroMediterranean, 19: 34-47.
  12. Porretta S, Gere A, Radványi D,Moskowitz H (2019) Mind Genomics (Conjoint Analysis): The new concept research in the analysis of consumer behaviour and choice. Trends in food science & technology, 84: 29-33.

Designing Eco-Serbia for High School Students: Using AI Simulation with Mind Genomics Thinking and Technology to Inspire Critical Thinking by a Gamifying a Topic

DOI: 10.31038/MGSPE.2024443

Abstract

AI (Chat GPT3.5) embedded in the Mind Genomics platform (Idea Coach feature of BimiLeap.com), was used to synthesize a teaching approach for critical thinking. The exercise, strictly based on interaction with AI, was guided by the request of students in the Gymnasium of Novi Sad, Serbia, to create a tool which would allow them to experience critical thinking in a gamified manner. The process and prompts are presented in this paper, with the path to create a new game, Eco-Serbia. The paper shows how to introduce the topic, the power of AI to simulate discussions about a topic, to create the specifics for the game, and then shows additional analyses. These additional analyses, done after the project has been ‘closed’ by the user. include showing how to discover key ideas, themes, perspectives, and types of responses by different audiences. The paper finishes with a detailed opportunity analysis of four innovations about eco-Serbi as suggested by the AI [1-4].

Introduction

Teaching gymnasium-level students critical thinking is vital; it empowers them to analyze information, evaluate arguments, and reach informed conclusions. Developing critical thinking skills enables students to sharpen their awareness of the world around them, prompting them to question assumptions, form their own opinions, and approach challenges with innovative solutions. The result is enhanced academic achievements while being equipped with the skills to navigate complex challenges in a diverse and interconnected world [5-9].

Integrating critical thinking into the high school gymnasium curriculum ignites student creativity, inspiring them to generate innovative ideas and solutions. Through the application of critical thinking skills across subjects such as science, literature, and history, students increase their understanding, interact meaningfully with the material, and enjoy a sense of curiosity and intellectual exploration. This method boosts academic success while igniting a lasting love for learning, inspiring students to engage in critical thinking across all areas of their lives.

Gamifying Strategy: Create a Game About the Topic, One Requiring and Rewarding Critical Thinking

Gamifying by using AI to create a game for eco-tourism in Serbia adds a level of excitement and engagement for the students. By turning the process into a game, it becomes more interactive and enjoyable, making the learning experience more fun and memorable. The outcome of making the AI effort focused on gamifying the project is more motivated students, enthusiastic about the task at hand. They will be more likely to invest their time and creativity into the project, leading to a higher quality final product [10-13].

When our Serbian gymnasium students in Novi Sad use AI and Mind Genomics-based thinking to create a game for eco-tourism in Serbia, we expect them to feel a sense of accomplishment and pride in their work. They will likely be more excited and engaged in the project, as they see it as a fun challenge rather than a tedious academic task. By gamifying the effort, they will be more likely to collaborate with their peers, think outside the box, and push themselves to create something unique and innovative [14-17].

By using AI to transform the creation of the eco-tourism game for Serbia into a gamification project, we hope to foster creativity, critical thinking, and a passion for innovation among our students. This approach will not only make the learning process more enjoyable and engaging but also equip them with valuable skills and experiences to apply in their future endeavors.

The remainder of this paper shows the approach, done virtually all with the aid of generative AI, specific Chat GPT3.5, using the Mind Genomics platform (www.BimiLeap.com). The AI is available through the Idea Coach feature. The set-up portions of the program are done by prompts easily created by the user. The detailed analysis after the program is closed, is done by a set of embedded prompts in the BimiLeap. Program and returns automatically for each iteration after the project is closed. The results done here required about 90 minutes. The deeper analysis was returned after the AI had finished its deeper analysis.

Phase 1 – ‘Listening in on the Conversations Among 10 Students When They Hear About the Project

Observing a conversation from the sidelines offers a glimpse into the thoughts, emotions, and viewpoints of individuals regarding a specific subject. The genuine voices of individuals discussing a topic offer an authenticity that written sources often lack. The subtleties of tone, body language, and emotions in spoken conversations enrich the understanding of the topic at hand.

Engaging in conversations about a topic fosters a deeper connection and understanding that reading from a textbook or article simply cannot achieve. Hearing firsthand accounts and personal anecdotes allows us to connect more deeply with the topic, revealing its significance in our own lives. Conversation’s human element weaves empathy and understanding into the fabric of discourse, essential for unraveling the complexities of any topic.

Additionally, being a ‘fly on the wall’ enables us to observe the intricate dynamics of group communication and the interplay of diverse perspectives and opinions. This opportunity allows us to broaden our perspectives and question our beliefs as we engage with the diverse viewpoints shared in the discussion. Engaging deeply in the conversation allows us to grasp a fuller and richer understanding of the subject matter.

In the realm of gamification, listening to discussions on a topic can offer essential insights for crafting captivating and interactive experiences. By grasping the subtleties of how individuals discuss and interact with a subject, game developers can craft gameplay that is more immersive and meaningful. Incorporating real-life dialogue and scenarios into games can make them more authentic, relatable, and ultimately more enjoyable for players. Table 1 shows the instructions for the AI to be a fly on the wal, and the results generated by AI.

Table 1: The fly on the wall’ strategy to simulate a conversation about Eco-Serbia.

Phase 2 – Using Detailed Instructions to AI to Create the Game

To craft a game, the AI must produce questions that are both stimulating and demanding, while also being enjoyable and captivating for the players. Incorporating elements of surprise, humor, or suspense into the questions can achieve this, along with varying the difficulty level or topic to engage a diverse group of players. The goal is to craft an engaging and lively game experience that captivates players and encourages them to return for more excitement. Using the AI’s cognitive skills and computational strength, game developers can craft tailored and immersive gaming experiences that resonate with the varied preferences and interests of players. The aim is to use AI’s potential to enrich the gaming experience, crafting more interactive and enjoyable games into which players can immerse themselves (Table 2).

Table 2: The AI-simulated created of the Eco-Serbia project, showing the instruction to the AI, and the steps returned to create the game.

Phase 3 – ‘Harvesting and Answering Questions Generated by AI at the End of Iteration 1, as well as Questions Asked in Iteration 2, and Finally Questions Emerging When AI Reviewed the Material After the Study was Closed

AI in platforms like Idea Coach on BimiLeap.com consistently prompts relevant questions and when instructed answers to those questions. These questions highlight important issues related to the topic. Inserting these questions into a BimiLeap iteration is simple, regardless of the topic’s origin or relevance. The user directs the AI, through the Idea Coach feature, to respond to the question using Chat GPT3.5 in a format relevant to the user. Table 3 shows 25 questions and answers generated by AI and answered by AI. These 25 questions and answers may overlap, but in the interests of showing what can be learned, all questions and answers are included.

Table 3: AI generated questions about the topic, and AI generated answers to those questions.

Phase 3: AI as Teacher of Critical Thinking by Reviewing the Original AI Output Automatically After the Study is Closed for Further Iteration

What are the Key Ideas?

The key ideas generated by AI in a topic provide a concise summary of the main concepts and themes within the subject matter (Table 4). Knowing the key ideas allows students to quickly grasp the most important information, enabling them to better understand and absorb the material. To find these key ideas, students can use tools such as text analysis software or utilize critical thinking skills to identify recurring themes and main points. Once the key ideas are determined, the next steps involve further exploring and analyzing them to deepen comprehension and stimulate critical thinking.

Table 4: Key Ideas

Finding key ideas and utilizing them to teach critical thinking is essential for developing students’ analytical skills and ability to extract meaningful information from complex topics. By focusing on key ideas, students learn to identify the most important elements of a subject and distinguish between relevant and irrelevant information. This process encourages critical thinking by prompting students to evaluate, question, and form their own opinions based on the key concepts presented.

What are the Themes and How do These Themes Manifest Themselves as Perspectives?

By exploring the themes highlighted by AI in the Mind Genomics analysis, individuals uncover a richer understanding of the patterns and foundational concepts embedded in the data. Examining the expression and interconnection of these themes allows researchers to glean valuable insights and pinpoint key takeaways from the study. A young researcher in Serbia can gain fresh insights and inspiration by exploring the list of themes, enriching their grasp of the subject matter. Through the examination of themes, students enhance their critical thinking abilities, recognizing patterns, forging connections, and deriving conclusions from the presented data. This approach prompts students to engage in analytical thinking, evaluate information with a critical eye, and present well-reasoned arguments to back their conclusions. The list of themes can act as a springboard for further research, igniting curiosity and encouraging students to explore the subject matter more deeply.

Students can enhance their critical thinking skills by examining the underlying assumptions and implications of each theme in the list. They can investigate the links between various themes and reflect on how these elements enhance the overall comprehension of the subject. Students can assess the relevance of each theme, gauge its impact on the research findings, and pinpoint any gaps or inconsistencies that may arise.

The themes identified through AI analysis provide a useful resource for enhancing critical thinking skills in education. Through a thoughtful exploration of themes, students can sharpen their analytical skills, foster logical thinking, and empower themselves to make well-informed decisions. This approach deepens understanding of the subject while inspiring students to tackle research and problem-solving with critical and analytical thinking (Table 5).

Table 5: Key themes recurring in the material generated by AI about the Eco-Serbia project, and the emerging perspectives.

Interested Audiences versus are Opposing Audiences

Understanding the interested and opposing audiences for the Eco-Serbia project, along with its gamification element, can significantly enhance our efforts in multiple ways. Identifying the audience interested in the topic enables us to customize our messaging and strategies, ensuring we engage and educate them effectively. This understanding enables us to craft focused campaigns and initiatives that connect with our audience, fostering increased participation and support for the project. Recognizing opposing audiences allows us to foresee challenges and objections, empowering us to tackle them head-on and refine our strategy to reduce resistance.

Additionally, grasping the perspectives of both supportive and opposing audiences can enhance critical thinking skills for participants and stakeholders alike. Through the examination of various viewpoints and the recognition of possible conflicts, individuals are prompted to engage in critical thinking regarding the project and its consequences. This cultivates a deeper insight into the relevant issues and promotes engaging conversation and discussion. Engaging with diverse viewpoints and opposing arguments allows participants to sharpen their analytical skills and make informed decisions regarding the project.

Integrating AI to analyze interested and opposing audiences in the Eco-Serbia project can significantly boost the effectiveness of its gamification elements. By grasping the preferences and viewpoints of various audience segments, we can customize the gamification elements to resonate more effectively with their interests and motivations. This focused method increases engagement and participation, paving the way for a more effective gamification strategy. By examining opposing viewpoints, we uncover potential challenges or concerns in the gamification process and tackle them proactively, creating a smoother and more enjoyable experience for all participants

Overall, it is important to address these potential oppositions by promoting the benefits of critical thinking, problem-solving, and environmental awareness, and by emphasizing the positive impact that the game can have on students’ education and understanding of the world around them (Table 6).

Table 6: Interested versus opposing audiences

Suggested Innovations – Analysis of Each from the Viewpoints and Business Opportunity, Respectively

The AI analysis prompts individuals to critically evaluate the project’s business dimensions. These dimensions include social aspects, uniqueness as well as business-case aspects. AI encourages individuals to foresee and tackle possible criticisms from investors, competitors, and other essential stakeholders, enabling researchers to craft a stronger and more convincing case for the gamification of the Eco-Serbia project. This thorough examination not only guides decision-making but also sharpens individuals’ capacity to strategically maneuver through intricate and layered challenges, ultimately boosting their critical thinking abilities along the way.

AI encourages individuals to question assumptions, challenge prevailing beliefs, explore alternative perspectives, and engage in strategic decision-making. The result is a culture of innovation, collaboration, and sustainability, crucial for tackling complex environmental challenges. Through critical analysis and reflection, individuals can cultivate stronger, more effective, and sustainable solutions for the Eco-Serbia project and beyond (Table 7).

Table 7: Deep AI analysis of four AI-suggested innovations.

Discussion and Conclusions

Making intellectual topics into games has greatly improved critical thinking by letting people interact with and think about knowledge in a fun and engaging way. Adding game features like challenges, puzzles, and competition encourages people to think critically and solve problems in order to win the game. This process helps people become better at analyzing things and pushes them to look at complicated issues from different points of view and think differently.

Gamification lets people use their critical thinking skills in the real world, making it easier to learn about and understand complicated topics through hands-on activities. People can improve their critical thinking skills in a safe and controlled setting by putting decision-making, strategy-building, and information processing into games. This makes them better at critical thought and gets them ready to use these skills in school, work, and personal life.

Using gamification as a tool helps people think more critically and interact with and learn more about intellectual topics in a more interesting and engaging way. This method turns learning into something fun and satisfying, which helps people understand and appreciate difficult topics more. By turning intellectual topics into games, people can connect with information in a fun and active way. This encourages exploration, analysis, and questioning, which leads to a deeper and more complete learning experience.

Adding game elements to educational topics could change the way people learn and improve their ability to think critically. Adding game features to intellectual and educational settings makes it easier for people to interact with and learn more about difficult topics in a fun and active way. This not only improves their critical thinking, but it also gives them the tools they need to deal with problems, face difficulties, and think deeply about different situations. By turning intellectual topics into games, people can start an interesting and exciting learning journey that can change them.

Acknowledgment

The authors gratefully acknowledge the ongoing help of Vanessa M Arcenas in the preparation of this manuscripts of the others in this grouping.

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Simulating and Stimulating Critical Thinking Using Mind Genomics Turbocharged with AI: Homelessness in the America of Tomorrow, 2030

DOI: 10.31038/MGSPE.2024442

Abstract

This paper presents a new, AI-based computer approach to simulate as well as stimulate critical thinking and generate potential innovations in a topic chosen by the researcher. The presentation here speculates with the help of AI regarding the outlook for homelessness in th United States. The paper shows the different stages of critical thinking for this topic and provides examples of how to develop a critical thinking mind-set. The technology is resident in a publicly available website, www.BimiLeap.com.

Introduction

Homelessness is a widespread issue in the United States, with approximately 500,000 people experiencing homelessness on any given night. The causes of homelessness are complex and varied, including factors such as lack of affordable housing, poverty, mental illness, and substance abuse. Those experiencing homelessness often face numerous challenges, including lack of access to necessities such as food and shelter, as well as barriers to employment and healthcare [1-4]

Artificial intelligence (AI) has the potential to play a significant role in addressing homelessness in the future. AI can be used to analyze data and identify trends in homelessness, allowing policymakers to make more informed decisions about resource allocation and intervention strategies. AI can also be used to develop predictive models that can anticipate homelessness trends and help target interventions to those most at risk. Additionally, AI can be used to streamline processes and improve efficiency in the delivery of services to individuals experiencing homelessness [5-9]

This paper presents an attempt use generative AI (Chat GPT3.5) accessed and prompted by Idea Coach, a feature in the Mind Genomics platform, BimiLeap.com. the approach shows how to use AI to study homelessness as a problem, using simulations (fly on the wall technique), AI-generated questions and answers, and then critical thinking about the results suggested by AI. The paper finishes with using AI to generate 10 suggested innovations, and for each innovation the paper shows how AI can dissect the innovation into its components and business opportunities.

Phase 1 – The Fly on the Wall Strategy Simulated through AI

The phrase “fly on the wall” refers to being able to listen in on a talk without being seen. In this case, “fly on the wall” refers to both that action and knowing what someone is thinking and why they are saying something. Being a fly on the wall in a meeting gives one a unique view of conversations and exchanges that might not have been seen otherwise. Oen can learn more about people’s goals and motivations by listening in on their conversations and hearing their private thoughts. This can be especially helpful when talking about touchy or controversial issues, because it lets one hear other points of view without getting involved in the conversation directly (Table 1).

Table 1: ‘Fly on the wall’ strategy at local meeting in ‘Smallville, USA, a town with coping with homelessness.

Besides that, being a fly on the wall gives one some privacy and objectivity. One can listen to the discussion without taking part and form their own opinions based on what they hear. This can help one learn more about a complicated issue, like homelessness in oner neighborhood, by gathering information about it. Being a “fly on the wall” can also help one figure out political goals and hidden agendas that are not talked about publicly. One can find out about underlying tensions or alliances which may affect decision-making by listening in on private thoughts and responses. This is often very important for getting a sense of how a meeting or group really works. Being a “fly on the wall” also lets one get a better sense of how people talk to each other and who has power in a group. One can figure out alliances and hierarchies that aren’t clear at first glance by watching who asks what questions and how others answer. This can help one understand how choices are made and who has power in a certain situation [10-13] (Table 1).

Phase 2 – Jumpstarting Learning by Instructing the AI to Create Sets of 15 Questions and Answers

Working with AI to create questions and answers benefits critical thinking by promoting creativity, prompting exploration of different angles, improving problem-solving abilities, enhancing analytical skills, and fostering innovation. The Idea Coach feature of BimiLeap.com, the Mind genomics platform, can come up with 15 different questions detailed answers in less than 30 seconds. For a deep analysis in a very short time, the process can be done almost effortlessly, with an iteration completed every half minute, for a total of 300 questions and answers in 10 minutes. Across all 300 questions about a third to a half will be unique, and not repeats. The benefit for critical thinking is clear, if only that AI can, in a virtually automated fashion, produce thousands of questions and answers in an hour, sufficient for a rapid education in the topic [14-17] (Table 2).

Table 2: Questions and Answers from Iteration #21.

Critical Thinking – Letting AI Review Its Own Questions, and Identify Questions Which Were Missing

After the ‘study’ is closed, the AI reviews the material it created, shown in Tables 1 and 2. AI then identifies questions that may have been ‘missed’ being asked, and presents them. Table 3 shows the remaining 10 questions identified by AI in its ‘self-review’. These 10 questions were run separately, with the same instructions as given in Table 2. Table 3 shows the remaining 10 questions and their answers. The number is consistent with that used in Tables 1 and 2 [18-21].

Table 3: Questions discovered by AI not to have been asked previously by AI, with their AI-generated answers.

Critical Thinking – Key Ideas and Themes in the 15 Original Questions and Answers

Dividing the topic of homelessness into key ideas, identifying themes, can significantly enhance critical thinking skills, particularly when working on a do-it-yourself project. By delving into various perspectives, one can gain a deeper understanding of the complexities surrounding the issue of homelessness and develop a more well-rounded approach to addressing it. This process forces individuals to think critically about the root causes of homelessness, the societal factors at play, and the potential solutions that can be implemented. Table 4 shows the key ideas and the themes [22-24].

Table 4: AI-abstracted key ideas and themes.

Critical Thinking – Alternative Views and Systematizing Them Through Perspectives

People are pushed to question their own views and biases when they look at things from different points of view. This leads to a more open-minded and objective analysis of the issue at hand. By doing this, people not only improve their minds, but they also start to think about more options and answers. Finding perspectives in the topic of homelessness can also help people organize their thoughts and ideas in a way that makes sense. This makes it easier to see patterns and links between different points of view (Table 5).

Table 5: Alternative viewpoints regarding topics involved in homelessness, and formalized analysis of these differences through perspectives.

By looking at things from different points of view, people can see the problem of homelessness from more than one angle, which helps them understand and care about those who are homeless. People can think about different points of view and approaches, which can help them come up with more creative and useful answers. Overall, breaking up ideas into different points of view, formalizing the through perspectives and exploring themes forces people to think more deeply and critically [25-27].

Critical Thinking – Which Audiences are Likely to be Interested Versus Which Audiences are Likely to Oppose

Understanding the accepting and opposing audiences allows one to more deeply understand multiple perspectives before making decisions related to homelessness in Smallville township. By understanding the potential supporters and detractors of each proposed solution, one can anticipate challenges and resistance, and adjust the approach accordingly. This level of critical thinking helps identify potential pitfalls and ensures that the proposed solutions have a higher chance of successful implementation (Table 6).

Table 6: Responses of interested versus opposing audiences.

The understanding of responses by different audiences lets one consider the feasibility and impact of each solution in a more nuanced way. By recognizing the diverse viewpoints within the community, one ca approach problem-solving with a more holistic understanding of the situation. This process ultimately leads to more effective and sustainable solutions for addressing homelessness in the Smallville township (Table 6).

Critical Thinking – ‘Deep Dive’ Analysis of 10 AI-suggested Innovations

With AI’s ability to analyze vast amounts of data and identify patterns, it can suggest innovative solutions that may have been overlooked by human researchers. Additionally, AI can provide real-time feedback on the effectiveness of these inventions, allowing for quick adjustments and improvements. This can greatly accelerate the progress in tackling homelessness and provide more efficient and effective solutions for those in need.

However, it is important to critically analyze the potential drawbacks of relying too heavily on AI in addressing homelessness. Whereas AI can provide valuable insights and recommendations, it may lack the empathy and understanding that human intervention can offer. It is essential to strike a balance between using AI as a tool for innovation and ensuring that human intervention and support are still prioritized in addressing the complex and nuanced issue of homelessness.

A standard feature of the BimiLeap.com platform is that at the end of each iteration, and after having reviewed all the information generated in that iteration, the AI suggests innovations, and for each innovation does a ‘deep dive’. That ‘deep dive’ looks at the nature of the innovation, the explanation of the innovation, its importance, uniqueness, attractiveness and degree of expected social good. The ‘deep dive’ finishes with slogans, and then with the different facets of a ‘business pitch’. Table 7 shows the real depth of the analysis for the 10 innovations [28-33].

Table 7: ‘Deep dive’ analysis of 10 innovations generated during one iteration (#21).

Discussion and Conclusions

Critical thinking is essential for solving complex issues like homelessness, as it helps individuals weigh options based on facts and common sense. It helps avoid relying on assumptions or stereotypes, and allows individuals to see things from different perspectives. Critical thinking also helps individuals question the status quo and come up with new solutions, ensuring solutions are based on diverse experiences and perspectives. It also encourages a mindset of continuous learning and improvement, allowing for flexible and adaptable solutions. For instance, if one is unfamiliar with homelessness, critical thinking can help them understand the main causes, current laws, and available tools to help homeless individuals find stable housing. Overall, critical thinking is a useful skill for people who want to deal with tough social problems like homelessness. People can make real changes to help end homelessness in their community by keeping an open mind, asking deep questions, looking for solid proof, and coming up with creative solutions.

Within this framework, the strategy of simulating and stimulating critical thinking using the Mind Genomics platform provides a promising tool, perhaps a tutorial. In a matter of minutes up front, and with output later on, the user can try out one, two, even a dozen or more alternative scenarios and issues, with the analysis automatic, simple, rapid, easy to understand, and occasionally even profound.

Acknowledgments

The ‘research stimulations’ for this paper emerged from iteration 21 (Results 21_ using AI access through the Idea Coach feature of BimiLeap.com. The AI, Chat GPT3.5, provided all the AI-based material. Bimileap.com is openly available for public use at a modest platform fee. BimiLeap.com is the platform for the emerging science of Mind Genomics, the inspiration for the work shown here.

The authors wish to thank Vanessa M. Arcenas for her ongoing help in preparing this and other manuscripts in this series.

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Subsurface Geopressure Prediction: Perception and Pitfalls

DOI: 10.31038/GEMS.2024674

Abstract

In this short article, a review of the subsurface geopressure forecast’s characteristic in relation to geological building blocks is briefly presented. Geopressure is the driving mechanisms of generating and migrating of oil and gas to their traps. Geopressure is a double edge sword. Most of the large hydrocarbon pools are embedded in this pressure zone, and on the other hand it can be a serious hazard for drilling crews and properties. The lack of understanding the prediction process can resulted in unintended miscalculation and ambiguous interpretation. The science of predicting the sedimentary column pressure profile drift is widely diverse and mostly driven by algorithms rather than the exploration prospect’s geological settings.

Introduction

Pore pressure prediction (PPP) is a process synonym of estimating the subsurface pressure before and post drilling. However, predictions should apply only if the calculation takes place before drilling. On the other hand, any estimation or assessment of the pressure from drilling records and logs data should not be considered as forecasts. They are pressure calibration or simulation instead of prediction. Subsurface pressure compartments are the response of sedimentation process, that lead to compaction and burial. Sand and shale/clay dominate most of the deltaic and shallow marine environments. Compaction due to sediment load leads to fluids expulsion out of the rock matrix that leads to increase pressure. Terzaghi and Peck (1948) [1] illustrated this process with a mechanical device filled with water. Most of the geopressure scholars believe the clastic sedimentary column is divided to two sections: Normal and Abnormal pressure (geopressure) zones. Eaton (1975) [2] made a successful leap to build the base model of calculating subsurface pressure increasing with depth using petrophysical properties (sonic, resistivity and density). The assumption of two main subsurface zones were utilized in his assessment as well. Shaker (2015) [3,4] recognized a new concept in the presence of 4 pressure zones in offshore and 3 zones in onshore in the clastic sedimentary columns. Based on the new established subsurface compartmentalization, a geological based prediction model of the subsurface geopressure is attained. This was an eye opener for some of the unintended pitfalls of geopressure prediction methods.

The Basic Geological Framework

Sediments reach the depositional basin carried by water through the water shed feeder areas. Suspended detrital grains begin the slow consolidation and compaction throughout time and additional load. During the periods of high sea stand, capable competent seals form. The top seal represents the choking barrier for the subsurface fluids outflux. This is referred to as the top of geopressure (TOG). Studying many subsurface petrophysical properties behavior led to the updated conclusion of subdividing most of subsurface clastic into four main pressure compartments [3,4). This is because the change in porosity and consequently pore pressure directly impacts the rock sonic velocity, electric resistivity, and density. Figure 1 exhibits, in a nut-shell, the relationship between 15 million years of compartmentalized sedimentary sequence, velocity (sonic ∆t) profile drift due to compaction, pressure in psi, and the designated four zones (after shaker, 2019) [5]. The compaction drift of the data (sonic/resistivity etc.) in zone B [4,5] follows an exponential trend. The deeper extension of this trend is used to estimate the pore pressure (Figure 1).

Figure 1: Exhibits P-D conceptual cartoon plot represents a 15 million years four zones (A,B,C,D) of high sea stand shale beds interbedded with reservoir sand beds . Shale Velocity drift with depth reflects the pressure increase changes across the shale beds. CT is the compaction trend. Red arrows represent the Effective Stress [1,2]. The right display is in linear scale (exponential compaction trend), whereas the log display on the left is in logarithmic scale (linear compaction trend).

The following are some basic criteria for pressure prediction calculations:

  1. Predicting pore pressure (PP) is before drilling and calibrations/simulation are during and post drilling.
  2. Predicting PP is always in the section below the Top of Geopressure [1,2,5].
  3. Petrophysical data should be representing the clean shale/clay lithology only [1, 2 ].
  4. Normal hydrostatic pressure resides in the very shallow zone (A) only [3,4,5].
  5. The deeper extrapolation of zone B compaction trend is used only to calculate the Pore Pressure (PP) in zones C (top seal) and the compartmentalized geopressured zone D [2,5]. It is designated as CT instead of NCT by [5].
  6. Calibration of the predicting pressure’s model should not rely solely on the measured PP (MPP) in reservoir sands. Mud logs and drilling records should be collaborated in this process [5].

Pitfalls

Most of the pitfalls in calculation of subsurface pressure is driven by the lack of geological and geomechanical building blocks input in the prediction model. Since excess pressure generation and causes are the product of stressed water bearing formation, principal and minimum stresses vectors should be known in addition of the overburden gravity vector (Shaker,2024) [6]. Applying the old theorem that subsurface pressure profile is divided to two segments (Normal and Abnormal pressure zones) separated by the TOG can cause substantial miscalculations (Figure 2). This is because the misleading assumption of considering the compaction trend data set represents a normally pressured sequence (NCT). Compaction and expulsion of fluids by differential pressure is not normally hydrostatic pressure gradient [5]. Sequence stratigraphy also can be a guidance to geopressure compartmentalization and assessing sealing verses breaching reservoirs (Shaker, 2002) [7]. Calibrating and simulating the predicted pore pressure in the shale with the measured pp using the wireline tools in the reservoir can lead to substantial calculation errors. This due to the fact that most of the effective stress methods are designed for shale beds [1,2]. Figure 3 shows the prediction modeling blunder if the prediction model is enforced to follow the measured pressure value data. Utilizing pressure prediction software does not include the manipulation of extracting certain lithology or the flexibility of maneuvering the stresses vectors especially in salt basins and can be a main source of unintended pitfalls [6].

The symptoms of pitfalls are usually revealed on the interpreted pressure plots such as:

  1. Predicting pressure data trace (values) facing a porous/permeable lithology especially in reservoir sands/sandstones e.g. Bowers, 1995 [8], Ehsan M. et al. 2024 [9], and Merrell et al. 2014 [10].
  2. Extrapolating a Normal Compaction Trend (NCT) that covers the entire drilled lithological section four zones e.g. Berry et al. (2003) [11].
  3. Swaying and breaking the NCT to separate segments for the purpose of matching the predicted pp in the shale to the measured pp in the sand reservoirs e.g. Kuyken. and de Lange (1999) [12].
  4. Predicting pp in salt basins utilizing the Overburden as principal stress (S1), regardless the effect of salt – sediments differential stresses and salt buoyancy e.g. Shaker and Smith 2002 [13], Merrell et al. 2014 [10], and Zhang and Yin, 2017 [14].

In summary, geopressure prediction modeling is a product of multi-disciplinary geoscience and engineering fields of expertise. Therefore, collaboration between these different disciplines can improve and enhance forecasting and simulate a bonified subsurface pressure profile.

Figure 2: P-D plot showing correlation between Predicting seismic Velocity – Pressure model using the conventional NCT vs. CT/4 zones. The data using NCT shows ambiguous profile whereas prediction data using the CT/4 zone shows a bonified prediction with an agreement with the data extracted from nearby offset well.

 
 

Figure 3: Shows two P-D plots for the same deepwater well. On the left panel measured reservoir’s pp (RFT) only used as calibration tool for Predicting PP in shale (PPP shale). Note the mismatch between the circle and arrow zones. On the right panel is the right prediction method that using the shale only for prediction modeling.

Acknowledgement

Special Thanks to Kate Mariana of the research open world for facilitate publishing this short article.

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