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Artificial Intelligence/Machine Learning Applications in the Oil and Gas Industry

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H: Geo-Energy".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 14801

Special Issue Editors

Department of Geoscience and Petroleum, Norwegian University of Science and Technology, S. P. Andersens veg 15b, 7031, Trondheim, Norway
Interests: reservoir modeling; enhanced oil recovery (EOR); CO2 EOR and storage; well test analysis
SINTEF Industry, Department of Petroleum, S.P. Andersens vei 15B, Trondheim, NO-7031, Norway
Interests: CO2 geological storage; CO2 enhanced oil recovery; reservoir modelling

Special Issue Information

Dear Colleagues,

We have the pleasure of inviting submissions to a Special Issue of Energies on the subject area of “Artificial Intelligence/Machine Learning Applications in the Oil and Gas Industry”.

While more efficient algorithms and high-performance computers can enhance the speed and accuracy of numerical models, they are incapable of transforming these models to new levels of computational footprint. Artificial intelligence and machine learning (AI&ML) have been used in petroleum engineering applications, with the possibility of combining the advantages of both traditional and intelligent modeling approaches to develop more powerful and faster computational tools. AI&ML capabilities perceive the relationship among relevant data and develop models based on the available measurements or simulated data. This characteristic makes the data-driven approach a viable modeling technology specifically for cases with complex physics. Promising results have been obtained in the application of data-driven techniques for resolving a wide variety of modeling problems such as history matching, well placement, production forecasting, injection strategies, CO2 storage and optimization and many more.

Dr. Ashkan Jahanbani Ghahfarokhi
Dr. Alv-Arne Grimstad
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
  •  machine learning
  •  data analytics
  •  petroleum engineering
  •  oil and gas industry
  •  CO2 utilization and storage
  •  reservoir engineering
  •  optimization
  •  digitalization
  •  real-time reservoir management

Published Papers (9 papers)

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Research

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22 pages, 785 KiB  
Article
Lithofacies Identification from Wire-Line Logs Using an Unsupervised Data Clustering Algorithm
Energies 2023, 16(24), 8116; https://doi.org/10.3390/en16248116 - 17 Dec 2023
Viewed by 618
Abstract
Stratigraphic identification from wire-line logs and core samples is a common method for lithology classification. This traditional approach is considered superior, despite its significant financial cost. Artificial neural networks and machine learning offer alternative, cost-effective means for automated data interpretation, allowing geoscientists to [...] Read more.
Stratigraphic identification from wire-line logs and core samples is a common method for lithology classification. This traditional approach is considered superior, despite its significant financial cost. Artificial neural networks and machine learning offer alternative, cost-effective means for automated data interpretation, allowing geoscientists to extract insights from data. At the same time, supervised and semi-supervised learning techniques are commonly employed, requiring a sufficient amount of labeled data to be generated through manual interpretation. Typically, there are abundant unlabeled geophysical data while labeled data are scarcer. Supervised and semi-supervised techniques partially address the cost issue. An underutilized class of machine-learning-based methods, unsupervised data clustering, can perform consonant classification by grouping similar data without requiring known results, presenting an even more cost-effective solution. In this study, we examine a state-of-the-art unsupervised data clustering algorithm called piecemeal clustering to identify lithofacies from wire-line logs, effectively addressing these challenges. The piecemeal clustering algorithm groups similar wire-log signatures into clusters, determines the number of clusters present in the data, and assigns each signature to one of the clusters, each of which represents a lithofacies. To evaluate the performance, we tested the algorithm on publicly released data from ten wells drilled in the Hugoton and Panoma fields of southwest Kansas and northwest Oklahoma, respectively. The data consist of two major groups: marine and non-marine facies. The study herein is centered around addressing two fundamental research questions regarding the accuracy and practicality of the piecemeal clustering algorithm. The algorithm successfully identified nine distinct clusters in our dataset, aligning with the cluster count observed in previously published works employing the same data. Regarding mapping accuracy, the results were notable, with success rates of 81.90% and 45.20% with and without considering adjacent facies, respectively. Further detailed analysis of the results was conducted for individual types of facies and independently for each well. These findings suggest the algorithm’s precision in characterizing the geological formations. To assess its performance, a comprehensive comparative analysis was conducted, encompassing other data clustering algorithms, as well as supervised and semi-supervised machine learning techniques. Notably, the piecemeal clustering algorithm outperformed alternative data clustering methods. Furthermore, despite its unsupervised nature, the algorithm demonstrated competitiveness by yielding results comparable to, or even surpassing, those obtained through supervised and semi-supervised techniques. Full article
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19 pages, 5955 KiB  
Article
Proxy Model Development for the Optimization of Water Alternating CO2 Gas for Enhanced Oil Recovery
Energies 2023, 16(8), 3337; https://doi.org/10.3390/en16083337 - 09 Apr 2023
Cited by 4 | Viewed by 1575
Abstract
Optimization studies are an important task in reservoir engineering practices such as production optimization and EOR (Enhanced Oil Recovery) assessments. However, they are extensive studies with many simulations that require huge computational effort and resources. In terms of EOR, CO2 injection is [...] Read more.
Optimization studies are an important task in reservoir engineering practices such as production optimization and EOR (Enhanced Oil Recovery) assessments. However, they are extensive studies with many simulations that require huge computational effort and resources. In terms of EOR, CO2 injection is one of the most common methods employed due to a high recovery potential and environmental benefits. To assess the feasibility of CO2-EOR projects, a reservoir design study must be conducted before optimization is performed. Some studies have demonstrated the advantages of employing proxy models to perform this task in terms of saving huge amounts of computer memory space and time. In this study, proxy models were developed to solve a multi-objective optimization problem using NSGA-II (Non-dominated Sorting Genetic Algorithm II) in two selected reservoir models. The study was performed for a CO2-WAG (Water Alternating Gas) application, where gas and water injection rates and half-cycle lengths were assessed to maximize the oil recovery and CO2 stored in the reservoir. One model represents a simple geological model (the Egg Model), while the other represents a complex model (the Gullfaks Model). In this study, the good performance of the proxy models generated accurate results that could be improved by increasing the amount of sampling and segmenting the behavior of the reservoir model (depending on the complexity of the reservoir model). The developed proxies have an average error of less than 2% (compared with simulation results) and are concluded to be robust based on the blind test results. It has also been found that to reach the maximum oil recovery using CO2-WAG, the maximum gas injection rate with the minimum water injection rate is required. However, this configuration may result in a reduction in the total CO2 stored in the reservoir. Full article
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26 pages, 10305 KiB  
Article
Fast Well Control Optimization with Two-Stage Proxy Modeling
Energies 2023, 16(7), 3269; https://doi.org/10.3390/en16073269 - 06 Apr 2023
Viewed by 941
Abstract
Waterflooding is one of the methods used for increased hydrocarbon production. Waterflooding optimization can be computationally prohibitive if the reservoir model or the optimization problem is complex. Hence, proxy modeling can yield a faster solution than numerical reservoir simulation. This fast solution provides [...] Read more.
Waterflooding is one of the methods used for increased hydrocarbon production. Waterflooding optimization can be computationally prohibitive if the reservoir model or the optimization problem is complex. Hence, proxy modeling can yield a faster solution than numerical reservoir simulation. This fast solution provides insights to better formulate field development plans. Due to technological advancements, machine learning increasingly contributes to the designing and building of proxy models. Thus, in this work, we have proposed the application of the two-stage proxy modeling, namely global and local components, to generate useful insights. We have established global proxy models and coupled them with optimization algorithms to produce a new database. In this paper, the machine learning technique used is a multilayer perceptron. The optimization algorithms comprise the Genetic Algorithm and the Particle Swarm Optimization. We then implemented the newly generated database to build local proxy models to yield solutions that are close to the “ground truth”. The results obtained demonstrate that conducting global and local proxy modeling can produce results with acceptable accuracy. For the optimized rate profiles, the R2 metric overall exceeds 0.96. The range of Absolute Percentage Error of the local proxy models generally reduces to 0–3% as compared to the global proxy models which has a 0–5% error range. We achieved a reduction in computational time by six times as compared with optimization by only using a numerical reservoir simulator. Full article
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30 pages, 7489 KiB  
Article
Application of Digitalization in Real-Time Analysis of Drilling Dynamics Using Along-String Measurement (ASM) Data along Wired Pipes
Energies 2022, 15(23), 8930; https://doi.org/10.3390/en15238930 - 25 Nov 2022
Cited by 1 | Viewed by 1559
Abstract
An automated drilling system requires a real-time evaluation of the drilling bit during drilling to optimize operation and determine when to stop drilling and switch bits. Furthermore, in the dynamic modeling of drill strings, it is necessary to take into account the interactions [...] Read more.
An automated drilling system requires a real-time evaluation of the drilling bit during drilling to optimize operation and determine when to stop drilling and switch bits. Furthermore, in the dynamic modeling of drill strings, it is necessary to take into account the interactions between drilling bits and rock. To address this challenge, a hybrid approach that combines physics-based models with data analytics has been developed to handle downhole drilling measurements in real time. First, experimental findings were used to formulate mathematical models of cutter–rock interaction in accordance with their geometrical characteristics, rock properties, and drilling parameters. Specifically, these models represent the normal and contact forces of polycrystalline diamond compact cutters (PDCs). Experimental data are analyzed utilizing deep learning, nonlinear regression, and genetic algorithms to fit nonlinear equations to data points. Following this, the recursive least square was implemented as a data analytic method to integrate real-time drilling data, drilling bit models, and mathematical models. Drilling data captured by the along-string measurement system (ASM) is implemented to estimate cutting and normal forces, torque, and specific energy at the bit. The unique aspect of this research is our approach in developing a detailed cutter–rock interaction model that takes all design and operation parameters into account. In addition, the applicability of the algorithm is demonstrated by real-time assessments of drilling dynamics, utilizing downhole digital data, that enable the prediction of drilling events and problems related to drilling bits. Full article
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15 pages, 5408 KiB  
Article
Data-Driven Proxy Models for Improving Advanced Well Completion Design under Uncertainty
Energies 2022, 15(20), 7484; https://doi.org/10.3390/en15207484 - 11 Oct 2022
Cited by 4 | Viewed by 1222
Abstract
In order to improve the design of advanced wells, the performance of such wells needs to be carefully assessed by taking the reservoir uncertainties into account. This research aimed to develop data-driven proxy models for the simulation and assessment of oil recovery through [...] Read more.
In order to improve the design of advanced wells, the performance of such wells needs to be carefully assessed by taking the reservoir uncertainties into account. This research aimed to develop data-driven proxy models for the simulation and assessment of oil recovery through advanced wells under uncertainty. An artificial neural network (ANN) was employed to create accurate and computationally efficient proxy models as an alternative to physics-based integrated well–reservoir models created by the Eclipse® reservoir simulator. The simulation speed and accuracy of the data-driven proxy models compared to physic-driven models were then evaluated. The evaluation showed that while the developed proxy models are 350 times faster, they can predict the production of oil and unwanted fluids through advanced wells with a mean error of less than 1% and 4%, respectively. As a result, the data-driven proxy models can be considered an efficient tool for uncertainty analysis where several simulations need to be performed to cover all possible scenarios. In this study, the developed proxy models were applied for uncertainty quantification of oil recovery from advanced wells completed with different types of downhole flow control devices (FCDs). According to the obtained results, compared to other types of well completion design, advanced wells completed with autonomous inflow control valve (AICV) technology have the best performance in limiting the production of unwanted fluids and are able to reduce the associated risk by 91%. Full article
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16 pages, 4722 KiB  
Article
Artificial Neural Network-Based Caprock Structural Reliability Analysis for CO2 Injection Site—An Example from Northern North Sea
Energies 2022, 15(9), 3365; https://doi.org/10.3390/en15093365 - 05 May 2022
Cited by 2 | Viewed by 1655
Abstract
In CO2 sequestration projects, assessing caprock structural stability is crucial to assure the success and reliability of the CO2 injection. However, since caprock experimental data are sparse, we applied a Monte Carlo (MC) algorithm to generate stochastic data from the given [...] Read more.
In CO2 sequestration projects, assessing caprock structural stability is crucial to assure the success and reliability of the CO2 injection. However, since caprock experimental data are sparse, we applied a Monte Carlo (MC) algorithm to generate stochastic data from the given mean and standard deviation values. The generated data sets were introduced to a neural network (NN), including four hidden layers for classification purposes. The model was then used to evaluate organic-rich Draupne caprock shale failure in the Alpha structure, northern North Sea. The train and test were carried out with 75% and 25% of the input data, respectively. Following that, validation is accomplished with unseen data, yielding promising classification scores. The results show that introducing larger input data sizes to the established NN provides better convergence conditions and higher classification scores. Although the NN can predicts the failure states with a classification score of 97%, the structural reliability was significantly low compare to the failure results estimated using other method. Moreover, this indicated that during evaluating the field-scale caprock failure, more experimental data is needed for a reliable result. However, this study depicts the advantage of machine learning algorithms in geological CO2 storage projects compared with similar finite elements methods in the aspect of short fitting time, high accuracy, and flexibility in processing different input data sizes with different scales. Full article
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Review

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53 pages, 3808 KiB  
Review
Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II
Energies 2023, 16(18), 6727; https://doi.org/10.3390/en16186727 - 20 Sep 2023
Cited by 2 | Viewed by 1386
Abstract
In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling [...] Read more.
In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications. Full article
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43 pages, 5391 KiB  
Review
Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I
Energies 2023, 16(16), 6079; https://doi.org/10.3390/en16166079 - 20 Aug 2023
Cited by 2 | Viewed by 1625
Abstract
In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling [...] Read more.
In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time- and resource-associated costs, and rendering reservoir simulators is not fast or robust, thus introducing the need for more time-efficient and smart tools like ML models which can adapt and provide fast and competent results that mimic simulators’ performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for cases of individual simulation runs and history matching, whereas ML-based production forecast and optimization applications are presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications. Full article
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17 pages, 1371 KiB  
Review
Review of Different Methods for Identification of Transients in Pressure Measurements by Permanent Downhole Gauges Installed in Wells
Energies 2023, 16(4), 1689; https://doi.org/10.3390/en16041689 - 08 Feb 2023
Viewed by 1241
Abstract
Permanent downhole gauges (PDG) are massively installed in injection and production wells operated in different industries such as oil and gas, geological CO2 storage, and the geothermal industry. These gauges provide a vast amount of real-time pressure measurements. The pressure measurements may [...] Read more.
Permanent downhole gauges (PDG) are massively installed in injection and production wells operated in different industries such as oil and gas, geological CO2 storage, and the geothermal industry. These gauges provide a vast amount of real-time pressure measurements. The pressure measurements may be divided into periods with a predominantly monotonic change of pressure in response to a sudden change of rate, called transients. These transients are caused by well operations, such as variation of injection or production rate and well shut-ins. Transient identification is one of the important steps in processing and interpreting the PDG data. Traditional transient identification is performed by processing and analyzing with human involvement, which is a step in post-operation well analysis. In modern well surveillance technology, permanent and reliable data transmission from the wellbore to the surface provide the possibility to analyze well performance in real time or proactively. So automated transient identification is a practical demand, but a challenge at the same time. This article starts with the definition of a transient, then reviews and compares seven methods for transient identification proposed by previous works available in the literature. A comparative analysis of these methods is carried out accounting for the detection algorithm and procedure, results of testing, and general positive and negative sides of performance and application of these methods. The results of this review facilitate further developments of field data interpretation techniques by the R&D community and academia and may help in the selection of a proper method for further application in well surveillance workflows developed in the industry. Full article
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