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Optimization and Simulation of Intelligent Oil and Gas Wells

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H1: Petroleum Engineering".

Deadline for manuscript submissions: closed (16 January 2023) | Viewed by 12634

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Special Issue Editors

Dr. Zhengming Xu

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Guest Editor

School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, China
Interests: wellbore multiphase flow and heat transfer; gas kick detection and handling; cutting transport in horizontal wellbore

Prof. Dr. Feifei Zhang

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Guest Editor

School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
Interests: intelligent drilling monitoring; Artificial Intelligence in drilling; drilling hydraulics; hole cleaning

Prof. Dr. Xiaofeng Sun

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Guest Editor

School of Petroleum Engineering, Northeast Petroleum University, Daqing 163318, China
Interests: fluid mechanics; hole cleaning; gas kick detection; gas-liquid two-phase flow

Dr. Qinzhuo Liao

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Guest Editor

College of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, China
Interests: machine learning; surrogate model; reservoir simulation; flow and transport in porous media
Special Issues, Collections and Topics in MDPI journals

Dr. Song Deng

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Guest Editor

School of Petroleum Engineering, Changzhou University, Changzhou 213000, China
Interests: artificial intelligence in oil and gas engineering; new energy and new materials for oil and gas exploration and development; carbon capture; utilization and storage(CCUS)

Dr. Zhaopeng Zhu

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Guest Editor

College of Mechanical and Transportation Engineering, China University of Petroleum-Beijing, Beijing 102200, China
Interests: artificial intelligence; parameter optimization; risk monitoring; controlled pressure drilling; KPI analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Improving oil and gas production efficiency and reducing costs have become the inevitable choice for oil companies to improve competitiveness and anti-risk ability. Oil companies are looking to improve the quality and management of their decisions across the industry chain through data analytics, real-time monitoring, and automation. The application of artificial intelligence improves how many of the problems related to the petroleum industry are solved from various standing points of view, including accuracy, robustness, and generalization.

This Special Issue aims to present and disseminate the most recent advances related to the application of artificial intelligence in well drilling, reservoir simulation, oil production, hydraulic fracturing, etc.

Topics of interest for publication include but are not limited to:

All aspects of well drilling, reservoir simulation, oil production, hydraulic fracturing, etc.
Trajectory control, risk detection, the rate of penetration prediction, risk management, and real-time prediction.
Cutting transport, gas–liquid two-phase flow, and critical transport velocity.
Gas kick, gas–liquid two-phase flow, kick handling, drilling fluid properties, and flow pattern recognition.
History matching, production forecasting, and production optimization.
Proppant transport, hydraulic fracturing design, and hydraulics optimization.

Dr. Zhengming Xu
Prof. Dr. Feifei Zhang
Prof. Dr. Xiaofeng Sun
Dr. Qinzhuo Liao
Dr. Song Deng
Dr. Zhaopeng Zhu
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.

Published Papers (9 papers)

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Research

18 pages, 6131 KiB

Open AccessArticle

Study on Erosion Model Optimization and Damage Law of Coiled Tubing

by Binqi Zhang, Jingen Deng, Hai Lin, Jie Xu, Guiping Wang, Wei Yan, Kongyang Wang and Fuli Li

Energies 2023, 16(6), 2775; https://doi.org/10.3390/en16062775 - 16 Mar 2023

Cited by 1 | Viewed by 1624

Abstract

Coiled tubing (CT) is used as a velocity string to transport high-velocity gas in drainage gas recovery technology. Sand particles flowing at high speed can cause serious erosion of the pipe wall. Long-term erosion wear leads to the degradation of the string strength and can even cause local perforation. In order to study the erosion wear problem of CT, a gas–solid erosion experimental device was established for a full-size pipe with different radii of curvature. A 3D laser confocal technique was used to examine and characterize the microscopic erosion morphology of the inner wall of the CT. The CFD erosion model was selected based on the erosion test data of the inner wall of the CT, and the erosion results of the Finnie model show minimal error and good agreement compared with other models. The average value of the error of the maximum erosion rate at different radii of curvature is 8.3%. The effect of the radius of curvature, gas velocity and solid particle size on the maximum erosion rate of the inner wall of the CT was analyzed based on the Finnie model. The results reveal that erosion wear occurs on the inner wall of the CT’s outer bend. As the radius of curvature is reduced, the maximum erosion rate and area increase, and the position of the maximum erosion rate gradually shifts toward the inlet. The maximum erosion rate is positively correlated with the gas flow rate. However, as the particle size increases, the maximum erosion rate shows a trend of first increasing, then decreasing and finally stabilizing, with a critical particle size of 200 μm. This study can provide theoretical guidance and methods for improving the service life of CT. The erosion rate of the tubing in old wells can be reduced by controlling production and employing appropriate sand control methods, while the erosion rate of tubing in new wells can be reduced by adjusting the wellbore trajectory. Full article

(This article belongs to the Special Issue Optimization and Simulation of Intelligent Oil and Gas Wells)

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13 pages, 2077 KiB

Open AccessArticle

Transfer Forest: A Deep Forest Model Based on Transfer Learning for Early Drilling Kick Detection

by Jiasheng Fu, Wei Liu, Xiangyu Zheng and Xiaosong Han

Energies 2023, 16(5), 2100; https://doi.org/10.3390/en16052100 - 21 Feb 2023

Cited by 2 | Viewed by 1324

Abstract

Kicks can lead to well control risks during petroleum drilling, and even more serious kicks may lead to serious casualties, which is the biggest threat factor affecting the safety in the process of petroleum drilling. Therefore, how to detect kicks early and efficiently has become a focus practical problem. Traditional machine learning models require a large amount of labeled data, such kicked sample, and it is difficult to label data, which requires a lot of labor and time. To address the above issues, the deep forest is extended to a transfer learning model to improve the generalization ability. In this paper, a transfer learning model is built to detect kicks early. The source domain model adopts the deep forest model. Deep forest is an ensemble learning model with a hierarchical structure similar to deep learning. Each layer contains a variety of random forests. It is an integration of the model in depth and breadth. In the case of a small sample size (20–60 min), kick can be identified 10 min in advance. The deep forest model is established as the source domaining model, and a cascade forest is added at the last layer according to the transfer learning algorithm to form the classification model of this paper. The experimental results show that the kick prediction accuracy of the model is 80.13% by a confusion matrix. In the target domain, the proposed model performs better than other ensemble learning algorithms, and the accuracy is 5% lower than other SOTA transfer learning algorithms. Full article

(This article belongs to the Special Issue Optimization and Simulation of Intelligent Oil and Gas Wells)

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18 pages, 9728 KiB

Open AccessArticle

An Improved Integrated Numerical Simulation Method to Study Main Controlling Factors of EUR and Optimization of Development Strategy

by Yihe Du, Hualin Liu, Yuping Sun, Shuyao Sheng and Mingqiang Wei

Energies 2023, 16(4), 2011; https://doi.org/10.3390/en16042011 - 17 Feb 2023

Cited by 1 | Viewed by 1206

Abstract

Gas reservoir numerical simulation is an important method to optimize the development strategy of shale gas reservoirs which has been influenced by the multi-stage fracture. The regular fracture network model was used to build a conventional numerical simulation, in which it was difficult to show the true situation of fracture propagation. However, the physical parameters not only affect the production, but also influence the stimulation effect; moreover, the quality of the fracturing effect also affects the production which causes the input and out parameters to be inaccurate. To solve this problem, the process simulation must be completed from geology to engineering to gas reservoir. The main controlling factors of production are identified with geological and engineering factors such as horizontal stage length, the volume of fracturing fluid, well spacing, production allocation, and proppant mass. Therefore, on the basis of the integrated simulation method of a hydraulic fracturing network simulation and an unstructured grid high-precision numerical simulation, this paper builds an integrated numerical simulation of a shale gas reservoir coupled with geology and engineering to optimize the development strategy with production as the target. Taking four wells of a platform as an example, the EUR (estimated ultimate recovery) has increased by 25% after the optimization of the development strategy. Full article

(This article belongs to the Special Issue Optimization and Simulation of Intelligent Oil and Gas Wells)

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15 pages, 6351 KiB

Open AccessArticle

A Comprehensive Multi-Factor Method for Identifying Overflow Fluid Type

by Zhenyu Tao, Honghai Fan, Yuhan Liu and Yuguang Ye

Energies 2023, 16(2), 922; https://doi.org/10.3390/en16020922 - 13 Jan 2023

Viewed by 1040

Abstract

Accurate identification of overflow fluid types facilitates timely and effective handling of onsite overflow accidents. Research into identifying the type of overflow fluid is limited, and there are only simple calculation models that do not consider enough effects; additionally, accuracy needs to be improved and the identification method is not perfect. If there is no drilling data, it is impossible to identify the overflow fluid. Therefore, this paper modifies the density calculation model of overflow fluid by considering the influence of the temperature, pressure field, and two-phase flow model, making the calculation result more accurate and universal, and puts forward a comprehensive method for auxiliary identification based on gas logging interpretation. This paper uses the gas state equation to verify the accuracy of the overflow density model; after verification using data from more than 20 overflowing wells, the method was found to be practical and had an accuracy rate of more than 90%. Thus, this study and the proposed method can provide guidance for dealing with overflow accidents in the field and any follow-up research. Full article

(This article belongs to the Special Issue Optimization and Simulation of Intelligent Oil and Gas Wells)

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12 pages, 4558 KiB

Open AccessArticle

An Improved Transformer Framework for Well-Overflow Early Detection via Self-Supervised Learning

by Wan Yi, Wei Liu, Jiasheng Fu, Lili He and Xiaosong Han

Energies 2022, 15(23), 8799; https://doi.org/10.3390/en15238799 - 22 Nov 2022

Cited by 2 | Viewed by 1139

Abstract

Oil drilling has always been considered a vital part of resource exploitation, and during which overflow is the most common and tricky threat that may cause blowout, a catastrophic accident. Therefore, to prevent further damage, it is necessary to detect overflow as early as possible. However, due to the unbalanced distribution and the lack of labeled data, it is difficult to design a suitable solution. To address this issue, an improved Transformer Framework based on self-supervised learning is proposed in this paper, which can accurately detect overflow 20 min in advance when the labeled data are limited and severely imbalanced. The framework includes a self-supervised pre-training scheme, which focuses on long-term time dependence that offers performance benefits over fully supervised learning on downstream tasks and makes unlabeled data useful in the training process. Next, to better extract temporal features and adapt to multi-task training process, a Transformer-based auto-encoder with temporal convolution layer is proposed. In the experiment, we used 20 min data to detect overflow in the next 20 min. The results show that the proposed framework can reach 98.23% accuracy and 0.84 F1 score, which is much better than other methods. We also compare several modifications of our framework and different pre-training tasks in the ablation experiment to prove the advantage of our methods. Finally, we also discuss the influence of important hyperparameters on efficiency and accuracy in the experiment. Full article

(This article belongs to the Special Issue Optimization and Simulation of Intelligent Oil and Gas Wells)

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15 pages, 4267 KiB

Open AccessArticle

Cuttings Bed Height Prediction in Microhole Horizontal Wells with Artificial Intelligence Models

by Yaotu Han, Xiaocheng Zhang, Zhengming Xu, Xianzhi Song, Weijie Zhao and Qilong Zhang

Energies 2022, 15(22), 8389; https://doi.org/10.3390/en15228389 - 10 Nov 2022

Cited by 2 | Viewed by 1297

Abstract

Inadequate drill cuttings removal can cause costly problems such as excessive drag, lower rate of penetration, and even mechanical pipe sticking. Cuttings bed height is usually used to evaluate hole-cleaning efficiency in horizontal wells. In this study, artificial intelligence models, including artificial neural network (ANN), support vector regression (SVR), recurrent neural network (RNN), and long short-term memory (LSTM), were employed to predict cuttings bed height in the well-bore. A total of 136 different tests were conducted, and cuttings bed height under different conditions were measured in our previous study. By training four different artificial intelligence models with the experiment data, it was found that the ANN model performed best among other artificial intelligence models. The ANN model outperformed the dimensionless cuttings bed height model proposed in our previous study. Due to the amount of data points, the memory ability of RNN and LSTM models has not been entirely played compared with the ANN model. Full article

(This article belongs to the Special Issue Optimization and Simulation of Intelligent Oil and Gas Wells)

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15 pages, 3195 KiB

Open AccessArticle

Real-Time Drilling Parameter Optimization Model Based on the Constrained Bayesian Method

by Jinbo Song, Jianlong Wang, Bingqing Li, Linlin Gan, Feifei Zhang, Xueying Wang and Qiong Wu

Energies 2022, 15(21), 8030; https://doi.org/10.3390/en15218030 - 28 Oct 2022

Cited by 1 | Viewed by 1577

Abstract

To solve the problems of the low energy efficiency and slow penetration rate of drilling, we took the geological data of adjacent wells, real-time logging data, and downhole engineering parameters as inputs; the mechanical specific energy and unit footage cost as multi-objective optimization functions; and the machine pump equipment limit as the constraint condition. A constrained Bayesian optimization algorithm model was established for the optimization solution, and drilling parameters such as weight-of-bit, revolutions per minute, and flowrate were optimized in real time. Through a comparison with NSGA-II, random search, and other optimization algorithms, and the application results of example wells, we show that the established Bayesian optimization algorithm has a good optimization effect while maintaining timeliness. It is suitable for real-time optimization of drilling parameters, can aid a driller in identifying the drilling rate and potential tapping area, and provides a decision-making basis for avoiding the low-efficiency rock-breaking working area and improving rock-breaking efficiency. Full article

(This article belongs to the Special Issue Optimization and Simulation of Intelligent Oil and Gas Wells)

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14 pages, 5073 KiB

Open AccessArticle

Fracture Mechanism of Crack-Containing Strata under Combined Static and Harmonic Dynamic Loads Based on Extended Finite Elements

by Haiping Zhang, Siqi Li, Zhuo Chen, Yeshuang Tong, Zhuolun Li and Siqi Wang

Energies 2022, 15(21), 7940; https://doi.org/10.3390/en15217940 - 26 Oct 2022

Cited by 1 | Viewed by 911

Abstract

Based on the existing research results, a theoretical fracture model of strata under the compound impact of static and harmonic dynamic load is improved, and the fracture characteristic parameters (stress intensity factor, T-stress, and fracture initiation angle) under the two far-field stress are determined according to the crack dip angle. Additionally, the effects of harmonic dynamic load on the distribution of the stress field and the fracture characteristic (the crack initiation angle, the fracture degree, the number of fracture units, and the fracture area) are further calculated and discussed by theoretical model solution, extended finite element simulation, and the secondary development of the simulation module, respectively. The research results show that the far-field stress, stress intensity factor, and T-stress vary in harmonic form with time under the compound impact of static and harmonic dynamic loads. The frequency of dynamic load affects the number of reciprocal fluctuations of stress intensity factor and T-stress as well as the crack initiation time, but has less influence on the crack initiation angle and fracture degree. While the amplitude of dynamic load affects the stress intensity factor, the extreme value of T-stress and fracture characteristics of the crack. This study has theoretical guiding significance for parameters’ optimization and realization of resonance impact drilling technology. Full article

(This article belongs to the Special Issue Optimization and Simulation of Intelligent Oil and Gas Wells)

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12 pages, 2146 KiB

Open AccessFeature PaperArticle

A New Method for Intelligent Prediction of Drilling Overflow and Leakage Based on Multi-Parameter Fusion

by Mu Li, Hengrui Zhang, Qing Zhao, Wei Liu, Xianzhi Song, Yangyang Ji and Jiangshuai Wang

Energies 2022, 15(16), 5988; https://doi.org/10.3390/en15165988 - 18 Aug 2022

Cited by 7 | Viewed by 1426

Abstract

The technical focus of drilling operations is changing to oil and gas reservoirs with higher difficulty factors such as low permeability and fracture. During the drilling process, drilling operations in deep complex formations are prone to overflow and leakage complications. Leakage and overflow problems will change the performance of the drilling fluid in the wellbore, impacting the wellbore pressure, and causing complex accidents such as stuck drilling and collapse. In order to improve the level of control over the risk of wellbore overflow and leakage, it is necessary to predict the mud overflow and leakage situation and to arrange and control the risk of leakage and overflow that may occur in advance to ensure the safety of drilling. By using a genetic algorithm to optimize the multi-layer feedforward neural network, this paper establishes a GA-BP Neural Network Drilling overflow and leakage prediction model based on multi-parameter fusion. Through the optimization training of 14 parameters that may affect the occurrence of complex downhole accidents, the mud overflow and leakage are predicted. The prediction results of the model are compared with the prediction results of a conventional BP neural network, and verified by the real drilling data. The results show that the MAE, MSE, and RMSE of the GA-BP neural network model are improved by 2.91%, 4.48%, and 10.93%, respectively, compared with the BP neural network model, and the prediction quality is higher. Moreover, the amount of mud overflow and leakage predicted by using the GA-BP neural network matches well with the pattern of mud overflow and leakage data in real drilling, which proves the effectiveness and accuracy of the GA-BP neural network in overflow and leakage prediction. Full article

(This article belongs to the Special Issue Optimization and Simulation of Intelligent Oil and Gas Wells)

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