About

D. Nathan Meehan is a Professor of Petroleum Engineering at Texas A&M University, holding the L.F. Peterson '36 Endowed Professorship. He is a member of the National Academy of Engineering and has been recognized with numerous awards including the Outstanding Alumni award from the University of Oklahoma, the Society of Petroleum Engineers Public Service Award, and the DeGolyer Distinguished Service Medal. His educational background includes a Ph.D. and M.S. in Petroleum Engineering from Stanford University and the University of Oklahoma, respectively, and a B.S. in Physics from Georgia Institute of Technology. His research interests encompass energy transition, carbon capture, use and storage, hydrogen production and storage, methane detection, lifecycle analysis of emissions, and efforts to decrease the carbon intensity of oil and gas production. He specializes in unconventional fields development, hydraulic fracturing, horizontal well technology, enhanced oil recovery, reservoir simulation, geostatistics, and oilfield leak detection and repair. Meehan has contributed to the field through his leadership roles, including serving as President of the Society of Petroleum Engineers, and has been actively involved in advancing petroleum engineering through his research, publications, and professional memberships.

Research topics

• Computer Science
• Political Science
• Business
• Physics
• Environmental science
• Engineering
• Natural resource economics
• Economics
• Geology
• Law
• Geography

Selected publications

• Can Artificial Intelligence and Machine Learning Predict the Performance of Nano-based Drilling Fluids? A Review

  Trends in Sciences · 2025-04-01 · 2 citations

  reviewOpen access

  Drilling fluids play a crucial role in the control and functionality of oil and gas well operations. Continuous monitoring, enhancement, and optimization of their properties are essential for successful drilling processes. Recently, a variety of additives, including nanoparticles (NPs) and novel polymers, have been introduced to modify and improve the performance of drilling fluids, addressing the emerging challenges in the field. The behavior of these fluids can change over time or under extreme drilling conditions, necessitating the use of predictive models to optimize their properties, particularly their rheological characteristics. In the past decade, there has been a growing trend of developing new models and correlations through artificial neural networks (ANN) and machine learning (ML) techniques within the petroleum industry. These methods enable the development of mathematical formulas that can predict the behavior of specific parameters based on known variables. Compared to traditional models, ANN and ML offer enhanced reliability and accuracy in predicting drilling fluid properties. This review aims to provide a comprehensive overview of the latest applications and mechanisms of various additives, with a particular focus on NPs, in drilling fluids. Additionally, it highlights the valuable insights and advancements in using ANN and ML techniques to predict and optimize the behavior of drilling fluids, which could pave the way for innovative applications and more efficient utilization of these technologies. HIGHLIGHTS The paper highlights the growing role of nanoparticles (NPs) and novel additives in enhancing drilling fluid performance, as well as the increasing use of Artificial Intelligence (AI) methods like Artificial Neural Networks (ANN) and Machine Learning (ML) for improved fluid management. NPs have demonstrated significant improvements in the properties of drilling fluids with promising potential for fluid performance in the field. The integration of ANN and ML with traditional models allows for more accurate predictions of drilling fluid behavior, providing better control over fluid properties and improving operational efficiency. Future research should focus on refining the existing models, exploring alternative environmentally friendly additives, and integrating AI models with advanced materials to enhance the sustainability and environmental safety of drilling fluids. GRAPHICAL ABSTRACT

  PublisherOA PDFDOI

• The National Academy of Engineering and the Society of Petroleum Engineering

  SPE Annual Technical Conference and Exhibition · 2025-10-13

  articleOpen access1st authorCorresponding

  Abstract The National Academy of Engineering (NAE), one of the three academies that comprise the National Academies of Sciences, Engineering, and Medicine (NASEM) [NAE, 2025], serves as an important advisory body to the U.S. federal government on matters involving engineering and technology. Established under the same 1863 congressional charter that created the National Academy of Sciences (NAS), the NAE brings together experts from academia, industry, government, and the nonprofit sector to provide consensus-based guidance on complex national challenges. This paper provides an overview of the NAE's organizational structure, membership selection process, and study methodologies, with a focus on its contributions to the energy domain. Particular attention is given to areas such as oil and gas, unconventional hydrocarbons, carbon capture and storage (CCS), and environmental safety. Numerous members of the Society of Petroleum Engineers (SPE) have been elected to the NAE, underscoring the importance of petroleum engineering in national science and engineering leadership. A dozen past Presidents of SPE are NAE members, as are scores of leading researchers and business leaders. While election to the NAE is widely recognized as a prestigious career milestone, the Academy's core function remains service-oriented—conducting studies and offering independent, expert advice to policymakers. The study process within NASEM is reviewed here, including notable examples such as safety recommendations following the Deepwater Horizon disaster [Arscott and Moreau, 2020], technical guidance on the Hubble Space Telescope repair [NRC,2005], formulation of fuel economy standards [NRC, 2002], and ongoing work on abandoned and orphaned wells [NASEM, 2025a]. The NAE's commitment to diversity across institutional sectors—academia, industry, government, and nonprofit (GNP) organizations—is paired with an emphasis on leadership, innovation, and engineering's broader societal impact [Meehan, 2015a], [Miller, 2019]. The paper also includes the author's reflections on participation in NAE initiatives and discusses how petroleum engineers can contribute more actively to the NAE's mission, including through efforts such as the Department of the Interior-sponsored study on orphaned and idled wells.

  PublisherOA PDFDOI

• The National Academy of Engineering and engineering professional societies

  Engineering Education Review · 2025-12-24

  articleOpen access1st authorCorresponding

  The National Academy of Engineering (NAE), one of the three academies operating under the National Academies of Sciences, Engineering, and Medicine, plays a pivotal role in advising the federal government on engineering and technology-related matters. Created under the same 1863 congressional charter that established the National Academy of Sciences (NAS), the NAE convenes experts from diverse backgrounds to provide consensus-driven guidance on pressing national issues. This paper presents an overview of the NAE's structure, membership, and study processes, highlighting its unique contributions. Most NAE members belong to one or more engineering professional societies (EPS); the relationship of NAE to these societies is reviewed. The purpose of this paper is to illuminate opportunities for collaborations between EPS and the NAE and demonstrate a high degree of existing collaboration between EPS members and the NAE. While election to the NAE is a significant honor for an engineer, the NAE exists primarily as a service organization, providing advice to the government and conducting scientific and practical studies. The process for election to the NAE is discussed. The study processes of National Academies of Sciences, Engineering, and Medicine (NASEM) are reviewed, with examples including impactful work in safety reform following the Deepwater Horizon disaster, guiding National Aeronautics and Space Administration (NASA)'s repair of the Hubble telescope, and the development of fuel economy standards. The NAE's commitment to a variety of memberships across academia, industry, government, and non-profits (GNP) organizations, as well as its emphasis on leadership, innovation, and societal impact, are discussed in detail. The paper discusses how engineers can contribute more actively to NAE's mission.

  PublisherOA PDFDOI

• An Ensemble-Based Automated Workflow for Decline Curve Analysis and Probabilistic Estimated Ultimate Recovery Estimation in Shale Gas Wells

  SPE Journal · 2025-10-01

  articleSenior author

  Summary Accurate estimation of estimated ultimate recovery (EUR) as training data is crucial for developing artificial intelligence (AI) tools to predict EURs. Decline curve analysis (DCA) is widely recognized for its efficiency in forecasting shale hydrocarbon production, yet current approaches face significant challenges. Existing methods often rely on type curves, which are insufficient for unconventional resources, and suffer from uncertainties related to data preprocessing, fitting techniques, and selection of DCA models. In this study, we introduce a novel “ensemble approach” that automates the DCA process for large data sets, addressing these challenges to facilitate efficient and consistent EUR estimation while accounting for uncertainties in the DCA process. Our novel workflow starts with automatic noise removal from production data using a user-defined moving window and advanced machine learning (ML) techniques, including K-nearest neighbor and local outlier factor (LOF), allowing users to confidently combine ML methods. Missing trends in decline curves are resolved using interpolation techniques, ensuring data continuity for effective fitting. Decline periods are validated algorithmically based on user-defined criteria. Initial fitting parameters are estimated automatically, with users selecting fitting techniques. To account for uncertainties, smoothing methods like locally weighted scatterplot smoothing (LOWESS) and Savitzky-Golay enable robust fitting of up to 50 DCA models per well. Seven DCA models are applied and rigorously evaluated using prediction accuracy metrics. Using this approach, we analyzed 666 shale gas wells in the Wolfcamp A and B intervals within minutes, while ensuring high reliability in the retained results by applying strict filtering based on adjusted R² and predictive accuracy above 90% and presenting probabilistic EUR distributions (P10, P50, P90). Insights into each DCA model’s performance were gained, which were essential for generating accurate type curves. Our approach is flexible, allowing users to adjust parameters at each step of the workflow. The workflow addresses the stability of each DCA model in terms of fitting convergence and noise sensitivity. For example, models such as Duong and Wang showed stable fitting convergence with small range of their fitting parameter values. However, the logistic growth (LGM) and power law exponential (PLE) models showed wide ranges of their fitting parameter values, indicating higher sensitivity to data preprocessing and initial estimates of the fitting parameters. This method introduces a comprehensive, ensemble-based approach to DCA that systematically addresses and minimizes uncertainties in DCA by integrating advanced preprocessing, noise removal, and robust fitting techniques. Scalable from single-well analyses to thousands of wells, it enables rapid and accurate reserve re-evaluation. Furthermore, it supports the development of AI-based EUR prediction tools by generating thousands of precisely estimated EUR data points, providing a reliable foundation for training and validation. It provides comparable EUR estimates to existing platforms by reducing overestimation, improving model stability, and offering deeper insights into fitting parameter ranges and sensitivity across various DCA models, though challenges remain in reliably identifying decline periods in wells with complex or irregular production profiles.

  PublisherDOI

• Value of Pore Space for Carbon Capture and Storage

  2025-01-01

  articleSenior author
  PublisherDOI

• Value of Offshore Pore Space for Carbon Capture and Storage

  Offshore Technology Conference · 2025-04-28 · 1 citations

  articleSenior author

  Abstract Most of the current operating carbon capture and storage (CCS) projects by volume utilize enhanced oil recovery for carbon storage; however, most proposed projects target saline aquifers. Ownership of pore space and rights to inject and store CO2offshore is straightforward; governments are beginning to lease such rights to prospective operators. What is the fair value for such pore space? Lease agreements could include both upfront payments, annual rentals and unit royalties. Oil and gas leasing offshore is mature, and most attractive leases hold the potential to attract competitive bids. How should governments and operators value pore space offshore? This paper discusses major issues and calculates example values. Issues that may impact valuations are also addressed. How much is such pore space worth? The owners of such pore space can be expected to be compensated for the rights to inject CO2 into these subsurface reservoirs. In oil and gas production and other mineral extraction cases, the valuation process is straightforward. There are a wide variety of methodologies to value the expected worth of rights to explore for, develop and produce oil and gas. This paper uses Monte Carlo simulation coupled with net present value calculations to characterize potential reservoirs, along with numerical reservoir simulations of CO2 injection into a range of potential storage targets. These targets vary in thickness, permeability and heterogeneity. The economic value of CCS projects is assessed for each case along with themaximum value of pore space for each case. The distribution of such values provides the expected value of the pore value. Existing models for valuing competitive bidding in offshore reservoirs are extended to generate bidding strategies for such reservoirs resulting in likely bids. Methods of assessing economic value of certain measurements designed to reduce risk are evaluated using the value of uncertain information approach. Finally, the impact of simple upfront versus upfront plus rental/royalty payments is considered. Primary drivers for valuation include the value of injected carbon, injectivity and storage capacity. The largest risks are containment and seismicity of sufficient magnitude to restrict operations. Pore space for reservoirs with high permeability and storage capacity is quite valuable; however, many realized cases result in negative present values for example cases. In such cases, any money spent by the operator for pore space only exacerbates economic loss. The value of uncertain information analysis indicates that risk reduction during the geological and geotechnical characterization phase can be extremely valuable. New methods are developed to estimate the value of pore space for CCS applications. Methods are illustrated for using the value of uncertain information in CCS pore space valuation. Integration of Monte Carlo simulation, reservoir simulation and bidding strategy for CCS pore space valuation is used for the first time.

  PublisherDOI

• Environmental Issues with Orphaned Wells

  SPE Annual Technical Conference and Exhibition · 2025-10-13

  articleOpen access1st authorCorresponding

  Summary Orphaned oil and gas wells in the United States, abandoned without a responsible party, pose significant and widespread risks to the environment and public health. These wells contribute to methane emissions, groundwater contamination, and chemical migration, yet they received little attention from policymakers until recent decades. Systemic problems, including inadequate funding, insufficient environmental monitoring, and inconsistent regulatory definitions, limit the effectiveness of current risk management and remediation efforts. This study integrates national geospatial data with environmental, infrastructure, and demographic datasets to evaluate proximity-based impacts of more than 117,000 documented orphaned wells. Our analysis shows that more than 4,000 schools, 300 hospitals, and thousands of domestic water wells lie within exposure zones determined using risk-specific buffer distances. For example, we used a 2,000-meter buffer for air quality and buffers from 762 to 3,000 meters for water resources, based on previous studies of air pollution and groundwater migration to provide conservative safety margins. Millions of Americans, with disproportionately high numbers among minorities, older adults, and people with disabilities, live within one mile of an orphaned well. Orphaned wells nearby also threaten environmentally sensitive areas, including national and local parks, major surface water bodies, federally designated critical habitats, and critical infrastructure, for example, power plants and hazardous waste treatment sites. This study also identifies the technical potential to repurpose well sites for geothermal, wind, and solar energy, as well as for subsurface energy storage. It presents new opportunities to address the orphaned well crisis, namely the use of voluntary carbon credit markets to fund plugging activities. Our results offer a comprehensive assessment of both risks and opportunities for remediating orphaned wells with limited funding.

  PublisherOA PDFDOI

• Risks and Challenges in CO2 Capture, Use, Transportation, and Storage

  Sustainability · 2025-09-01 · 7 citations

  articleOpen access1st authorCorresponding

  Reaching net-zero greenhouse gas emissions will require broad deployment of carbon capture and storage (CCS), yet significant challenges remain. This paper reviews the main barriers that may hinder or delay widespread CCS adoption, drawing on current projects in various stages of planning, construction, and development. The discussion focuses on technical, economic, social, and regulatory aspects of CCS and identifies several key obstacles. These include the high financial burden on energy production, persistent uncertainties about the long-term behavior of stored CO2, and the complexity of the regulatory framework governing CCS projects and CO2 pipelines. Carbon capture, use, and storage (CCUS) remains a major focus of attention in the petroleum industry due to its potential to remove carbon dioxide from the atmosphere or prevent future emissions. Despite this potential, challenges and risks continue to limit progress.

  PublisherOA PDFDOI

• Barriers to Sustainably Scaling Carbon Capture: Risks and Challenges in CO<sub>2</sub> Capture, Use, Transportation and Storage

  Preprints.org · 2025-07-18

  preprintOpen access1st authorCorresponding

  Reaching net-zero greenhouse gas emissions will require broad deployment of Carbon Capture and Storage (CCS), yet significant challenges remain. This paper reviews the main barriers that may hinder or delay widespread CCS adoption, drawing on current projects in various stages of planning, construction, and development. The discussion focuses on technical, economic, social, and regulatory aspects of CCS and identifies several key obstacles. These include the high financial burden on energy production, persistent uncertainties about the long-term behavior of stored CO2, and the complexity of the regulatory framework governing CCS projects and CO2 pipelines. Carbon capture, use, and storage (CCUS) remains a major focus of attention in the petroleum industry due to its potential to remove carbon dioxide from the atmosphere or prevent future emissions. Despite this potential, challenges and risks continue to limit progress.

  PublisherOA PDFDOI

• Harnessing AI and Computer Vision for Efficient Geothermal Field Exploration and Prospect Evaluation

  2025-01-01

  articleSenior author
  PublisherDOI

Frequent coauthors

• Tarek Ahmed

  City College of New York

  9 shared

• Craig Cipolla

  Hess (United States)

  4 shared

• Hassan M. El-Houjeiri

  Saudi Aramco (Saudi Arabia)

  3 shared

• Khosrow Biglarbigi

  3 shared

• Hitesh Mohan

  Inotek Pharmaceuticals (United States)

  3 shared

• Jeff Rutherford

  Stanford University

  2 shared

• M. P. Cleary

  2 shared

• Roland N. Horne

  Stanford University

  2 shared

Awards & honors

• Outstanding Alumni, Mewbourne College of Earth and Energy, U…
• President, Society of Petroleum Engineers – 2015-2016
• Public Service Award, Society of Petroleum Engineers – 2014
• DeGolyer Distinguished Service Medal, Society of Petroleum E…
• Lester C. Uren Award, Society of Petroleum Engineers – 1999