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Machine Learning Applications in Petroleum Industries and Geothermal Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H2: Geothermal".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 30243

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Special Issue Editors

Dr. Maruti Kumar Mudunuru

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Guest Editor

Pacific Northwest National Laboratory, Richland, WA 99354, USA
Interests: machine learning; deep learning; reduced-order modeling; artificial intelligence; computational mechanics; porous media modeling; geothermal; oil and gas; watersheds
Special Issues, Collections and Topics in MDPI journals

Dr. Kalyana Babu Nakshatrala

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Guest Editor

Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77004, USA
Interests: multiphysics modeling; flow through porous media; reactive-transport; continuum theories; machine learning; deep learning

Dr. Zhao Hao

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Guest Editor

Earth and Enviornmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Interests: machine learning applications; chemical imaging; chemical dynamics at mineral surfaces and interfaces; soil dynamics

Special Issue Information

Dear Colleagues,

We have organized a Special Issue on “Machine Learning Applications in Petroleum Industries and Geothermal Systems” in Energies, and we would like to invite you to contribute. The deadline for manuscript submission is 31 August 2021. Please feel free to disseminate this invitation within your group and among colleagues who may be interested.

Subsurface resources contribute to more than 80% of US energy resources (e.g., unconventional resources, geothermal energy) as well as 50% of US drinking water. The subsurface also serves as a reservoir for storing CO2 and energy waste. Therefore, optimizing subsurface resources in an environmentally friendly way is critical for energy security. Achieving this optimization requires transformative advances in our ability to characterize, model, monitor, engineer, and sustain these resources. Recent advances in machine learning (ML) have shown promise in developing capabilities to characterize subsurface energy systems. Specifically, new approaches based on ML can effectively utilize multiple datasets (e.g., geological, geophysical, hydrological, geochemical, remote sensing, distributed temperature sensing, distributed acoustic sensing, electromagnetic, InSAR, LiDAR, GPR) that can sense the subsurface and identify critical system transitions (e.g., stress, evolution of fracture networks). As a result, ML can accelerate the development of advanced process control approaches to manage and engineer the subsurface for enhanced energy production. Examples include development of reduced-order/surrogate models or emulators for predicting quantities of interest such as oil/water/gas production, geothermal energy production, and dominant fracture paths for fluid flow.

The goal of our Special Issue is to include comprehensive review papers, case-studies, short communications, recent results, and studies related to the application of ML for geothermal and petroleum industries. Applications may also include machine learning methods and data analytics to discover and exploit new subsurface signatures, engineer subsurface systems, estimate the state of the stress, increase hydrocarbon extraction efficiency from unconventional reservoirs, control and manipulate permeability, discover and exploit hidden/blind/enhanced geothermal systems, play fairway analysis, reduce the risk of geothermal energy development and exploration, optimize thermal power production through geothermal power plant or reservoir monitoring and analytics, improve prediction and detection of anomalous events during oil and gas and geothermal operations, and improve data analytical capabilities in geothermal operations.

This issue aims to bring ML researchers, geoscientists, hydrologists, oil and gas, and geothermal experts to address key questions, such as: How can we use machine learning and artificial intelligence tools to accelerate porous media model development, reduce simulation time, and detect more subsurface signatures from multiple datasets? How can we develop fast, reliable, and accurate emulators that can combine representative data across a range of scales to better calibrate process models? Topics of interest include but are not limited to:

Surrogate models or emulators for energy production, storage, and extraction;
Physics-informed machine learning for oil and gas and geothermal systems;
ML to discover and exploit geothermal resources;
ML-assisted inversion for subsurface imaging;
ML models for subsurface fluid flow, thermal, and/or reactive transport;
Artificial intelligence (AI) techniques/technologies/tools/software for subsurface resource management;
Machine learning approaches or workflows to improve and optimize data acquisition;
Advanced analytics for efficiency and automation in petroleum and/or geothermal operations;
Efficient ML models for data compression, in situ monitoring, and/or edge computing;
Explainable AI for geosciences;
Advanced uncertainty quantification using machine learning.

Dr. Maruti Kumar Mudunuru
Dr. Kalyana Babu Nakshatrala
Dr. Zhao Hao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Artificial intelligence
Data mining and data analytics
Machine learning
Deep learning
Neural networks
Geothermal systems
Oil and gas
Fracture networks
Energy conversion and storage

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Published Papers (8 papers)

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Research

16 pages, 3009 KiB

Open AccessArticle

Interpretable Feature Construction and Incremental Update Fine-Tuning Strategy for Prediction of Rate of Penetration

by Jianxin Ding, Rui Zhang, Xin Wen, Xuesong Li, Xianzhi Song, Baodong Ma, Dayu Li and Liang Han

Energies 2023, 16(15), 5670; https://doi.org/10.3390/en16155670 - 28 Jul 2023

Cited by 2 | Viewed by 1390

Abstract

Prediction of the rate of penetration (ROP) is integral to drilling optimization. Many scholars have established intelligent prediction models of the ROP. However, these models face challenges in adapting to different formation properties across well sections or regions, limiting their applicability. In this paper, we explore a novel prediction framework combining feature construction and incremental updating. The framework fine-tunes the model using a pre-trained ROP representation. Our method adopts genetic programming to construct interpretable features, which fuse bit properties with engineering and hydraulic parameters. The model is incrementally updated with constant data streams, enabling it to learn the static and dynamic data. We conduct ablation experiments to analyze the impact of interpretable features’ construction and incremental updating. The results on field drilling datasets demonstrate that the proposed model achieves robustness against forgetting while maintaining high accuracy in ROP prediction. The model effectively extracts information from data streams and constructs interpretable representational features, which influence the current ROP, with a mean absolute percentage error of 7.5% on the new dataset, 40% lower than the static-trained model. This work provides a theoretical reference for the interpretability and transferability of ROP intelligent prediction models. Full article

(This article belongs to the Special Issue Machine Learning Applications in Petroleum Industries and Geothermal Systems)

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11 pages, 4658 KiB

Open AccessCommunication

Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico

by Maruti K. Mudunuru, Bulbul Ahmmed, Elisabeth Rau, Velimir V. Vesselinov and Satish Karra

Energies 2023, 16(7), 3098; https://doi.org/10.3390/en16073098 - 29 Mar 2023

Cited by 6 | Viewed by 2461

Abstract

Geothermal energy is considered an essential renewable resource to generate flexible electricity. Geothermal resource assessments conducted by the U.S. Geological Survey showed that the southwestern basins in the U.S. have a significant geothermal potential for meeting domestic electricity demand. Within these southwestern basins, play fairway analysis (PFA), funded by the U.S. Department of Energy’s (DOE) Geothermal Technologies Office, identified that the Tularosa Basin in New Mexico has significant geothermal potential. This short communication paper presents a machine learning (ML) methodology for curating and analyzing the PFA data from the DOE’s geothermal data repository. The proposed approach to identify potential geothermal sites in the Tularosa Basin is based on an unsupervised ML method called non-negative matrix factorization with custom k-means clustering. This methodology is available in our open-source ML framework, GeoThermalCloud (GTC). Using this GTC framework, we discover prospective geothermal locations and find key parameters defining these prospects. Our ML analysis found that these prospects are consistent with the existing Tularosa Basin’s PFA studies. This instills confidence in our GTC framework to accelerate geothermal exploration and resource development, which is generally time-consuming. Full article

(This article belongs to the Special Issue Machine Learning Applications in Petroleum Industries and Geothermal Systems)

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17 pages, 6194 KiB

Open AccessFeature PaperArticle

Latent-Space Dynamics for Prediction and Fault Detection in Geothermal Power Plant Operations

by Yingxiang Liu, Wei Ling, Robert Young, Jalal Zia, Trenton T. Cladouhos and Behnam Jafarpour

Energies 2022, 15(7), 2555; https://doi.org/10.3390/en15072555 - 31 Mar 2022

Cited by 3 | Viewed by 1997

Abstract

This paper presents a latent-space dynamic neural network (LSDNN) model for the multi-step-ahead prediction and fault detection of a geothermal power plant’s operation. The model was trained to learn the dynamics of the power generation process from multivariate time-series data and the effects of exogenous variables, such as control adjustment and ambient temperature. In the LSDNN model, an encoder–decoder architecture was designed to capture cross-correlation among different measured variables. In addition, a latent space dynamic structure was proposed to propagate the dynamics in the latent space to enable prediction. The prediction power of the LSDNN was utilized for monitoring a geothermal power plant and detecting abnormal events. The model was integrated with principal component analysis (PCA)-based process monitoring techniques to develop a fault-detection procedure. The performance of the proposed LSDNN model and fault detection approach was demonstrated using field data collected from a geothermal power plant. Full article

(This article belongs to the Special Issue Machine Learning Applications in Petroleum Industries and Geothermal Systems)

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56 pages, 64533 KiB

Open AccessArticle

Machine Learning-Enhanced Play Fairway Analysis for Uncertainty Characterization and Decision Support in Geothermal Exploration

by R. Chadwick Holmes and Aimé Fournier

Energies 2022, 15(5), 1929; https://doi.org/10.3390/en15051929 - 7 Mar 2022

Cited by 6 | Viewed by 4063

Abstract

Geothermal exploration has traditionally relied on geological, geochemical, or geophysical surveys for evidence of adequate enthalpy, fluids, and permeability in the subsurface prior to drilling. The recent adoption of play fairway analysis (PFA), a method used in oil and gas exploration, has progressed to include machine learning (ML) for predicting geothermal drill site favorability. This study introduces a novel approach that extends ML PFA predictions with uncertainty characterization. Four ML algorithms—logistic regression, a decision tree, a gradient-boosted forest, and a neural network—are used to evaluate the subsurface enthalpy resource potential for conventional or EGS prospecting. Normalized Shannon entropy is calculated to assess three spatially variable sources of uncertainty in the analysis: model representation, model parameterization, and feature interpolation. When applied to southwest New Mexico, this approach reveals consistent enthalpy trends embedded in a high-dimensional feature set and detected by multiple algorithms. The uncertainty analysis highlights spatial regions where ML models disagree, highly parameterized models are poorly constrained, and predictions show sensitivity to errors in important features. Rapid insights from this analysis enable exploration teams to optimize allocation decisions of limited financial and human resources during the early stages of a geothermal exploration campaign. Full article

(This article belongs to the Special Issue Machine Learning Applications in Petroleum Industries and Geothermal Systems)

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18 pages, 723 KiB

Open AccessArticle

Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production

by Ivan Makhotin, Denis Orlov and Dmitry Koroteev

Energies 2022, 15(3), 1199; https://doi.org/10.3390/en15031199 - 7 Feb 2022

Cited by 12 | Viewed by 3358

Abstract

Waterflooding is a widely used secondary oil recovery technique. The oil and gas industry uses a complex reservoir numerical simulation and reservoir engineering analysis to forecast production curves from waterflooding projects. The application of such standard methods at the stage of assessing the potential of a huge number of projects could be computationally inefficient and requires a lot of effort. This paper demonstrates the applicability of machine learning to rate the outcome of waterflooding applied to an oil reservoir. We also explore the relationship of project evaluations by operators at the final stages with several performance metrics for forecasting. Real data about several thousand waterflooding projects in Texas are used in the current study. We compare the ML models rankings of the waterflooding efficiency and the expert rankings. Linear regression models along with neural networks and gradient boosting on decision threes are considered. We show that machine learning models allow reducing computational complexity and can be useful for rating the reservoirs, with respect to the effectiveness of waterflooding. Full article

(This article belongs to the Special Issue Machine Learning Applications in Petroleum Industries and Geothermal Systems)

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20 pages, 5426 KiB

Open AccessArticle

Modeling Subsurface Performance of a Geothermal Reservoir Using Machine Learning

by Dmitry Duplyakin, Koenraad F. Beckers, Drew L. Siler, Michael J. Martin and Henry E. Johnston

Energies 2022, 15(3), 967; https://doi.org/10.3390/en15030967 - 28 Jan 2022

Cited by 11 | Viewed by 4585

Abstract

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells—increasing or decreasing the fluid flow rates across the wells—and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. In this study, we describe a new approach combining reservoir modeling and machine learning to produce models that enable such a strategy. Our computational approach allows us, first, to translate sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy, and second, to find optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an “open-source” reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 h, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 s. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Full article

(This article belongs to the Special Issue Machine Learning Applications in Petroleum Industries and Geothermal Systems)

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20 pages, 5360 KiB

Open AccessFeature PaperArticle

A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)

by Grant Buster, Paul Siratovich, Nicole Taverna, Michael Rossol, Jon Weers, Andrea Blair, Jay Huggins, Christine Siega, Warren Mannington, Alex Urgel, Jonathan Cen, Jaime Quinao, Robbie Watt and John Akerley

Energies 2021, 14(20), 6852; https://doi.org/10.3390/en14206852 - 19 Oct 2021

Cited by 18 | Viewed by 4891

Abstract

Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as-built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data-driven thermodynamics approach, GOOML is able to accurately model the real-world performance characteristics of as-built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state-of-the-art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world. Full article

(This article belongs to the Special Issue Machine Learning Applications in Petroleum Industries and Geothermal Systems)

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24 pages, 5740 KiB

Open AccessArticle

Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

by Dongkwon Han and Sunil Kwon

Energies 2021, 14(12), 3629; https://doi.org/10.3390/en14123629 - 18 Jun 2021

Cited by 26 | Viewed by 4186

Abstract

Reservoir modeling to predict shale reservoir productivity is considerably uncertain and time consuming. Since we need to simulate the physical phenomenon of multi-stage hydraulic fracturing. To overcome these limitations, this paper presents an alternative proxy model based on data-driven deep learning model. Furthermore, this study not only proposes the development process of a proxy model, but also verifies using field data for 1239 horizontal wells from the Montney shale formation in Alberta, Canada. A deep neural network (DNN) based on multi-layer perceptron was applied to predict the cumulative gas production as the dependent variable. The independent variable is largely divided into four types: well information, completion and hydraulic fracturing and production data. It was found that the prediction performance was better when using a principal component with a cumulative contribution of 85% using principal component analysis that extracts important information from multivariate data, and when predicting with a DNN model using 6 variables calculated through variable importance analysis. Hence, to develop a reliable deep learning model, sensitivity analysis of hyperparameters was performed to determine one-hot encoding, dropout, activation function, learning rate, hidden layer number and neuron number. As a result, the best prediction of the mean absolute percentage error of the cumulative gas production improved to at least 0.2% and up to 9.1%. The novel approach of this study can also be applied to other shale formations. Furthermore, a useful guide for economic analysis and future development plans of nearby reservoirs. Full article

(This article belongs to the Special Issue Machine Learning Applications in Petroleum Industries and Geothermal Systems)

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