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Applications of Intelligent Models in the Petroleum Industry, 2nd Edition
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Applications of Intelligent Models in the Petroleum Industry, 2nd Edition

A Special Issue of Processes (ISSN 2227-9717) belonging to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1275

Editor

Dr. Gang Hui

E-Mail Website
Guest Editor
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Interests: machine learning; reservoir characterization; numerical simulation; geomechanics; hydraulic fracturing; unconventional reservoirs; induced seismicity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the second volume of the previous successful Special Issue, titled ‘Applications of Intelligent Models in the Petroleum Industry’.

The petroleum industry plays a critical role in the global energy landscape, supplying fuel and raw materials essential for modern economies. With the advent of advanced computing and data-driven technologies, intelligent models are revolutionizing petroleum exploration and production by offering more accurate, efficient, and adaptive solutions to complex problems.

This Special Issue on ‘Applications of Intelligent Models in the Petroleum Industry, 2nd Edition’ seeks high-quality research contributions that explore the integration of intelligent modeling techniques into various facets of petroleum engineering. The focus is on innovative developments in intelligent models and data analytics that enhance decision-making processes, optimize resource extraction, and improve operational efficiency. Topics of interest include, but are not limited to, the following:

  • Machine learning applications in reservoir characterization, such as permeability and porosity prediction, facies classification, and digital rock reconstruction;
  • AI-driven production forecasting models for conventional and unconventional reservoirs, incorporating time-series analysis, deep learning architectures, and hybrid modeling approaches;
  • Multimodal techniques that integrate tabular data, time-series, and images to enhance subsurface understanding and improve reservoir simulation models;
  • Physics-informed machine learning approaches that couple domain knowledge with data-driven methodologies to enhance model interpretability and generalization;
  • AI-powered real-time monitoring and predictive maintenance systems for drilling, wellbore stability, enhanced oil recovery (EOR), and equipment failure prediction;
  • Challenges such as data scarcity, quality assurance, and uncertainty quantification in intelligent model applications within the petroleum sector.

Dr. Gang Hui
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes 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 2400 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

  • oil and gas
  • digital oilfield
  • big data
  • machine learning
  • petroleum engineering
  • reservoir characterization
  • production forecasting
  • well test analysis
  • multimodal model
  • physics-informed model

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Related Special Issue

Published Papers (4 papers)

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Research

19 pages, 2487 KB  
Article
Oil Displacement Characteristics of High-Pressure CO2 Miscible Flooding and Optimization of Dynamic Adjustment Measures at Different Development Stages
by Shiming Zhang, Wenkuan Zheng, Dong Zhang, Chuanfei Wang, Rongtao Li, Zhongwei Wu, Yingzhu Guan and Haoyu Tang
Processes 2026, 14(19), 3040; https://doi.org/10.3390/pr14193040 - 22 Sep 2026
Abstract
Oil displacement characteristic curves provide an effective means of evaluating displacement performance. However, the displacement characteristics of high-pressure CO2 miscible flooding remain insufficiently understood. Dynamic adjustment measures and parameter optimization methods for different development stages also require further improvement. This study accounts [...] Read more.
Oil displacement characteristic curves provide an effective means of evaluating displacement performance. However, the displacement characteristics of high-pressure CO2 miscible flooding remain insufficiently understood. Dynamic adjustment measures and parameter optimization methods for different development stages also require further improvement. This study accounts for the compressibility of CO2, crude oil, and rock and uses seepage theory to construct characteristic charts for high-pressure CO2 miscible flooding. Existing stage classification methods are compared, and an improved method is developed by incorporating bottom-hole pressure variations. An integrated analytic hierarchy process (AHP)–entropy weight–technique for order preference by similarity to ideal solution (TOPSIS) method is then employed to optimize adjustment measures and their operating parameters. The method identifies the combinations that provide the best oil displacement and CO2 storage performance at different development stages. The results show that the actual curve of Well X1 in a representative well group in Block F of Shengli Oilfield bends upward and noticeably crosses the characteristic chart curves. Its development performance is therefore expected to deteriorate. The upward crossing is more pronounced for Well 8, which exhibits poor development performance. The improved method divides the entire CO2 flooding process into four stages: gas-free oil production, early gas channeling development, late gas channeling development, and storage. Injection–production coupling provides poorer oil displacement and CO2 storage performance than chemical profile control and plugging or water-alternating-gas flooding at all stages. Nevertheless, its low cost and operational flexibility support continued application. Water-alternating-gas flooding (WAG) gives the best performance at every stage. The optimal combination comprises 25 WAG cycles, a 4-month alternation period, a gas injection intensity of 3.33 t/(d·m), and a water–gas ratio of 3:1. These findings support performance evaluation and adjustment measure optimization for high-pressure CO2 miscible flooding. Full article
(This article belongs to the Special Issue Applications of Intelligent Models in the Petroleum Industry, 2nd Edition)
42 pages, 821 KB  
Article
Dual-Prior-Constrained Temporal Inversion and FNO Surrogate-Model-Driven Coordinated Optimization of Drilling Engineering Parameters
by Yue Ma, Feng Ni, Jihe Ma, Wenfa Qiu and Gang Hui
Processes 2026, 14(19), 3036; https://doi.org/10.3390/pr14193036 - 22 Sep 2026
Abstract
Real-time and accurate inversion of drilling-fluid hydraulic parameters while drilling, together with the coordinated optimization of drilling parameters, is critical for safe and efficient drilling in deep and complex formations. Conventional methods are limited by single-source observations, insufficient prior constraints, weak surrogate-model generalization, [...] Read more.
Real-time and accurate inversion of drilling-fluid hydraulic parameters while drilling, together with the coordinated optimization of drilling parameters, is critical for safe and efficient drilling in deep and complex formations. Conventional methods are limited by single-source observations, insufficient prior constraints, weak surrogate-model generalization, and isolated parameter optimization. To address these issues, this paper proposes a three-layer intelligent decision-making framework. The first layer is a dual-prior-constrained temporal inversion module that fuses a pre-drill mechanistic baseline prior with an offset-well statistical prior and estimates plastic viscosity, yield point, annular cuttings concentration, and equivalent eccentricity from standpipe-pressure and rotary-torque observations through a four-term loss function. The second layer is a Fourier neural operator (FNO) surrogate trained on data generated by an in-house two-phase hydraulics solver. All results reported here are obtained on such synthetic data: no field or laboratory measurements are used, and the offset-well statistical prior is prescribed—its mean from a regional depth trend and its covariance from an assumed inter-well variability—rather than fitted to measured offset-well logs. The third layer is a hydraulic–mechanical coupled multi-objective optimization framework that coordinates weight on bit, rotary speed, and flow rate using online Bayesian optimization and probabilistic safety constraints. Numerical experiments with 30 independent noise realizations show that the dual-prior constraints reduce the inversion root-mean-square error by up to 85% for the weakly identifiable parameters (cuttings concentration and equivalent eccentricity) and by 37% for plastic viscosity; that the FNO surrogate is about 240 times faster than the reference numerical simulation and attains a mean relative error of 0.31% for equivalent circulating density and 2.25% for annular pressure loss, about three times lower than the best baseline (a quadratic response surface, 0.95% and 7.16%), preserving that lead outside the training range, while remaining the only surrogate that can be evaluated on a different depth grid without retraining; and that three-parameter optimization improves the rate of penetration by 34.9%. At an equal total surrogate cost, the proposed online optimizer attains the highest mean gain of the methods compared (32.4% over 40 sliding windows, against 31.9% for an online NSGA-II with the same update frequency and 29.4% for a random-search control) while using 4.5 times fewer forward evaluations of the surrogate; the advantage over NSGA-II is small and not statistically resolved at this budget, whereas the advantage over random search is, and the proposed method also has the best worst-case window and the smallest window-to-window spread. The recommended operating points are re-verified with the reference forward solver. Imposing the two analytic closure relations of the forward model as soft physics residuals did not improve accuracy because the reference data satisfy them exactly and the constraint therefore carries no additional information. The framework provides an accurate, efficient, and robust solution for real-time intelligent drilling decision-making. Full article
(This article belongs to the Special Issue Applications of Intelligent Models in the Petroleum Industry, 2nd Edition)
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20 pages, 6854 KB  
Article
Fracture Development Probability Prediction in Tight Oil Reservoirs by Integrating Fracture Response Mapping with Triangular Topology-Optimized BiLSTM
by Jianchao Shi, Jiwei Wang, Xiaoke Li, Yongjian Feng, Qiang Liu, Wenyan Yang, Shuai Duan and Xinyu Li
Processes 2026, 14(15), 2475; https://doi.org/10.3390/pr14152475 - 31 Jul 2026
Viewed by 471
Abstract
Natural fractures strongly influence fluid flow, hydraulic-fracturing performance, and production heterogeneity in tight oil reservoirs. Their identification from conventional logs remains challenging because image-log and core coverage is limited, fracture-related logging responses are non-unique, and discrete fracture interpretations are difficult to align with [...] Read more.
Natural fractures strongly influence fluid flow, hydraulic-fracturing performance, and production heterogeneity in tight oil reservoirs. Their identification from conventional logs remains challenging because image-log and core coverage is limited, fracture-related logging responses are non-unique, and discrete fracture interpretations are difficult to align with regularly sampled logging sequences. This study used conventional logging data and electrical image-log interpretations from 17 wells in the Xifeng Oilfield, Ordos Basin, together with core observations from selected intervals, to develop a fracture response mapping and triangular topology-optimized bidirectional long short-term memory model (FRM-BiLSTM-TTAO). After sliding-window construction and density-based undersampling, 1713 samples were retained and partitioned at the well level into 14 training wells and three independent test wells, yielding an approximate training-to-test sample ratio of 75:25. FRM extracts lithologic-background, local-abrupt-change, multiscale-fluctuation, and integrated fracture response features; BiLSTM captures bidirectional depth dependencies; and TTAO selects fracture response features and optimizes the network architecture and training parameters. On the test set, the model achieved a ROC-AUC of 0.9079, a recall of 0.8671, and an F1-score of 0.8464, outperforming CNN, MLP, ResNet1D, XGBoost, and the corresponding ablation models. The predicted high-probability intervals were generally consistent with image-log interpretations and core observations, indicating the feasibility of the proposed method for identifying fracture-prone intervals within the study area. Full article
(This article belongs to the Special Issue Applications of Intelligent Models in the Petroleum Industry, 2nd Edition)
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25 pages, 15482 KB  
Article
An Attention-Based Deep Learning Method for Acoustic Emission Arrival Picking in True Triaxial Hydraulic Fracturing Experiments
by Ji Lu and Botao Lin
Processes 2026, 14(12), 2004; https://doi.org/10.3390/pr14122004 - 20 Jun 2026
Viewed by 394
Abstract
Accurate arrival picking of acoustic emission (AE) data is essential for AE event localization and hydraulic fracture characterization in true triaxial hydraulic fracturing experiments. However, conventional arrival picking methods are highly sensitive to manually defined thresholds, whereas existing deep learning models are constrained [...] Read more.
Accurate arrival picking of acoustic emission (AE) data is essential for AE event localization and hydraulic fracture characterization in true triaxial hydraulic fracturing experiments. However, conventional arrival picking methods are highly sensitive to manually defined thresholds, whereas existing deep learning models are constrained by low signal-to-noise ratios (SNRs) and limited AE dataset sizes. To address these challenges, this study proposes an attention-based deep learning method for AE arrival picking. The proposed method introduces an attention mechanism into the PhaseNet framework to suppress noise feature transmission in the skip connections. In addition, a kernel density estimation (KDE)-based label smoothing strategy was adopted to alleviate label imbalance and account for arrival-time uncertainty. The results demonstrate that the proposed method reduced the mean absolute error (MAE) by 10.58%, 92.92%, and 98.25% compared with PhaseNet, STA/LTA, and AR-AIC, respectively. The proposed method exhibited superior picking accuracy, robustness, and computational efficiency relative to the other methods, providing a reliable foundation for AE event localization and high-precision AE monitoring in hydraulic fracturing experiments. Full article
(This article belongs to the Special Issue Applications of Intelligent Models in the Petroleum Industry, 2nd Edition)
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