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Artificial Intelligence Applications in Petroleum Exploration and Production

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 25426

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Special Issue Editors

Dr. Hangyu Li

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Guest Editor

School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Interests: application of AI in petroleum field development, CO₂ EOR and geological storage

Dr. Xianzhi Song

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Guest Editor

State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, Beijing 102249, China
Interests: machine learning and big data in drilling and completion

Dr. Shuyang Liu

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Guest Editor

School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Interests: application of AI/ML in reservoir characterization and simulation; optimization of injection and production strategy; CO₂-EOR/EGR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in computer and data sciences have made artificial intelligence techniques a useful tool in tackling the problems in petroleum exploration and production. Intelligent exploration and production has become a hot topic both in academia and oil and gas companies. This Special Issue aims to solicit recent progress and best practices in the application of artificial intelligence techniques in petroleum exploration and production.

The following are some of the topics proposed for the Special Issue (not an exhaustive list):

Application of AI in geophysics;
Intelligent well log interpretation;
Machine learning and big data in drilling and completion;
Application of AI in petroleum production engineering;
Artificial intelligence in reservoir characterization;
Artificial intelligence in reservoir simulation;
Proxy modeling for history matching and production optimization;
Best practices and experiences in intelligent oil field pilot project.

Dr. Hangyu Li
Dr. Xianzhi Song
Dr. Shuyang Liu
Guest Editors

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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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

artificial intelligence techniques
machine learning
petroleum exploration
petroleum production
intelligent exploration and production
drilling and completion

Published Papers (12 papers)

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Editorial

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3 pages, 164 KiB

Open AccessEditorial

Artificial Intelligence Applications in Petroleum Exploration and Production

by Hangyu Li, Xianzhi Song and Shuyang Liu

Appl. Sci. 2023, 13(10), 6214; https://0-doi-org.brum.beds.ac.uk/10.3390/app13106214 - 19 May 2023

Viewed by 1580

Abstract

Recent advances in computer and data sciences have made artificial intelligence techniques a useful tool in tackling the problems in petroleum exploration and production [...] Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

Research

Jump to: Editorial

22 pages, 6328 KiB

Open AccessArticle

Intelligent Stuck Pipe Type Recognition Using Digital Twins and Knowledge Graph Model

by Qian Li, Junze Wang and Hu Yin

Appl. Sci. 2023, 13(5), 3098; https://0-doi-org.brum.beds.ac.uk/10.3390/app13053098 - 27 Feb 2023

Cited by 1 | Viewed by 1649

Abstract

During drilling operations, stuck pipe occurs from time to time due to various reasons such as continuous changes of the formation lithology, failure to return the drill cuttings in time, shrinkage or collapse caused by soaking the formation with drilling fluid, and steps in the well wall caused by the drill-down. After the stuck pipe, the identification of the stuck pipe type can only be guessed by manual experience due to the jamming of the drill stem downhole, which lacks a scientific basis. Moreover, there is a lack of studies on the stuck pipe type. Therefore, scientific and accurate identification of the stuck pipe type is of great significance for timely unsticking and resuming drilling. In this paper, based on the friction torque rigid rod model of a3D well track, we obtained the degree of deviation of measured parameters from the normal trend, which can scientifically evaluate the degree of stuck pipe. Based on the SAX morphological symbolic aggregation approximation method, we obtained the changing trend of measured parameters during the stuck pipe, which can accurately describe the change laws of characteristic parameters during the stuck pipe. Based on the statistical characterization laws of different stuck pipe types in Sichuan and Chongqing, we established the knowledge graph of stuck pipe types, which can correlate with the complex knowledge of different stuck pipe types. The stuck pipe type can be identified according to the degree of stuck pipe, the changing trend of the characteristic parameters of stuck pipe, and the knowledge graph of stuck pipe types. The results show that the method can combine digital twins and the knowledge graph to accurately identify the stuck pipe type and provide a basis for taking targeted deconstruction measures. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

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14 pages, 4030 KiB

Open AccessArticle

Drilling Parameters Optimization for Horizontal Wells Based on a Multiobjective Genetic Algorithm to Improve the Rate of Penetration and Reduce Drill String Drag

by Chuanzhen Zang, Zongyu Lu, Shanlin Ye, Xinniu Xu, Chuanming Xi, Xianzhi Song, Yong Guo and Tao Pan

Appl. Sci. 2022, 12(22), 11704; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211704 - 17 Nov 2022

Cited by 4 | Viewed by 2002

Abstract

With the development of China’s oil and gas exploration and development to complex oil and gas fields, the drilling efficiency and safety of complex formations with large hardness and strong abrasiveness have become increasingly significant. Optimizing drilling parameters is an effective means to increase the rate of penetration (ROP) and improve drilling efficiency. However, traditional drilling parameter optimization methods with only a single objective of increasing the ROP lack consideration of the drill string’s drag which may also be increased when drilling parameters change. When drilling a horizontal well, increased drag can reduce drilling efficiency. Aiming at this problem, this paper uses the logging data of the oil field as the data source, establishes an intelligent ROP prediction model through the random forest algorithm, and calculates the string drag using the “hard-string” model. Finally, the nondominant sorting genetic algorithm-II (NSGA-II), which is a domination-based multiobjective optimization algorithm, is used to optimize the drilling parameters to increase the ROP and reduce the drag at the same time. The optimized drilling parameters guide the drilling operations. We used the proposed method to optimize the parameters during the drilling of a new horizontal well. The results show that the ROP of the horizontal section of the new well increases by 10.3%, and the drag reduces by 4.5% on average compared with the adjacent well. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

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15 pages, 6559 KiB

Open AccessArticle

A Novel Cementing Quality Evaluation Method Based on Convolutional Neural Network

by Chunfei Fang, Zheng Wang, Xianzhi Song, Zhaopeng Zhu, Donghan Yang and Muchen Liu

Appl. Sci. 2022, 12(21), 10997; https://0-doi-org.brum.beds.ac.uk/10.3390/app122110997 - 30 Oct 2022

Cited by 6 | Viewed by 2183

Abstract

The quality of cement in cased boreholes is related to the production and life of wells. At present, the most commonly used method is to use CBL-VDL to evaluate, but the interpretation process is complicated, and decisions associated with significant risks may be taken based on the interpretation results. Therefore, cementing quality evaluation must be interpreted by experienced experts, which is time-consuming and labor-intensive. To improve the efficiency of cementing interpretation, this paper used VGG, ResNet, and other convolutional neural networks to automatically evaluate the cementing quality, but the accuracy is insufficient. Therefore, this paper proposes a multi-scale perceptual convolutional neural network with kernels of different sizes that can extract and fuse information of different scales in VDL logging. In total, 5500 datasets in Tarim Oilfield were used for training and validation. Compared with other convolutional neural network algorithms, the multi-scale perceptual convolutional neural network algorithm proposed in this paper can evaluate cementing quality more accurately by identifying VDL logging. At the same time, this model’s time and space complexity are lower, and the operation efficiency is higher. To verify the anti-interference of the model, this paper added 3%, 6%, and 9% of white noise to the VDL data set for cementing evaluation. The results show that, compared with other convolutional neural networks, the multi-scale perceptual convolutional neural network model is more stable and more suitable for the identification of cementing quality. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

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18 pages, 7267 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning-Assisted Prediction of Oil Production and CO₂ Storage Effect in CO₂-Water-Alternating-Gas Injection (CO₂-WAG)

by Hangyu Li, Changping Gong, Shuyang Liu, Jianchun Xu and Gloire Imani

Appl. Sci. 2022, 12(21), 10958; https://0-doi-org.brum.beds.ac.uk/10.3390/app122110958 - 29 Oct 2022

Cited by 8 | Viewed by 2394

Abstract

In recent years, CO₂ flooding has emerged as an efficient method for improving oil recovery. It also has the advantage of storing CO₂ underground. As one of the promising types of CO₂ enhanced oil recovery (CO₂-EOR), CO₂ water-alternating-gas injection (CO₂-WAG) can suppress CO₂ fingering and early breakthrough problems that occur during oil recovery by CO₂ flooding. However, the evaluation of CO₂-WAG is strongly dependent on the injection parameters, which in turn renders numerical simulations computationally expensive. So, in this work, machine learning is used to help predict how well CO₂-WAG will work when different injection parameters are used. A total of 216 models were built by using CMG numerical simulation software to represent CO₂-WAG development scenarios of various injection parameters where 70% of them were used as training sets and 30% as testing sets. A random forest regression algorithm was used to predict CO₂-WAG performance in terms of oil production, CO₂ storage amount, and CO₂ storage efficiency. The CO₂-WAG period, CO₂ injection rate, and water–gas ratio were chosen as the three main characteristics of injection parameters. The prediction results showed that the predicted value of the test set was very close to the true value. The average absolute prediction deviations of cumulative oil production, CO₂ storage amount, and CO₂ storage efficiency were 1.10%, 3.04%, and 2.24%, respectively. Furthermore, it only takes about 10 s to predict the results of all 216 scenarios by using machine learning methods, while the CMG simulation method spends about 108 min. It demonstrated that the proposed machine-learning method can rapidly predict CO₂-WAG performance with high accuracy and high computational efficiency under conditions of various injection parameters. This work gives more insights into the optimization of the injection parameters for CO₂-EOR. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

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15 pages, 4075 KiB

Open AccessArticle

Combining Knowledge and a Data Driven Method for Identifying the Gas Kick Type in a Fractured Formation

by Hu Yin, Menghan Si, Hongwei Cui, Qian Li and Wei Liu

Appl. Sci. 2022, 12(21), 10912; https://0-doi-org.brum.beds.ac.uk/10.3390/app122110912 - 27 Oct 2022

Cited by 1 | Viewed by 970

Abstract

The main forms of gas kicks into the wellbore during drilling in fractured carbonate reservoirs are underbalanced pressure and gravity displacement. These two forms of gas kicks have different mechanisms of gas entry into the wellbore and different well control measures, which require the timely identification of the type of gas kick when it occurs. A two-phase flow model with a wellbore-formation coupling was developed, based on the gas kick rate models. The variation characteristics of the bottomhole gas influx rate, the wellbore free gas, the bottomhole pressure, the bottomhole pressure change rate, the pit gain and the outlet flow rate during an underbalanced pressure gas kick and a gravity displacement gas kick, were compared and analyzed. Combining the dynamic time warping (DTW) and the wellbore-formation, coupled with a two-phase flow model, an identification method of the gas kick type, based on the DTW was proposed. Following the detection of the gas kick, the identification is performed by calculating the DTW distance between the surface parameter time series, obtained from the two-phase flow simulation and the surface parameter time series, measured in real time. The field example results show that the method can identify the type of gas kick, based on the real-time surface measurement parameters and provide a basis for taking targeted well control measures. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

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26 pages, 6233 KiB

Open AccessArticle

Generation of Synthetic Compressional Wave Velocity Based on Deep Learning: A Case Study of Ulleung Basin Gas Hydrate in the Republic of Korea

by Minsoo Ji, Seoyoon Kwon, Min Kim, Sungil Kim and Baehyun Min

Appl. Sci. 2022, 12(17), 8775; https://0-doi-org.brum.beds.ac.uk/10.3390/app12178775 - 31 Aug 2022

Cited by 1 | Viewed by 1359

Abstract

This study proposes a deep-learning-based model to generate synthetic compressional wave velocity (Vp) from well-logging data with application to the Ulleung Basin Gas Hydrate (UBGH) in the East Sea, Republic of Korea. Because a bottom-simulating reflector (BSR) is a key indicator to define the presence of gas hydrate, this study generates the Vp for identifying the BSR by detecting the morphology of the hydrate in terms of the change in acoustic velocity. Conventional easy-to-acquire logging parameters, such as gamma-ray, neutron porosity, bulk density, and photoelectric absorption, were selected as model inputs based on a sensitivity analysis. Long short-term memory (LSTM) and an artificial neural network (ANN) were used to design an efficient learning-based predictive model with sensitivity analysis for hyperparameters. The LSTM model outperforms the ANN model by preserving the geological sequence of the well-logging data. Ten-fold cross-validation was conducted to verify the consistency of the LSTM model and yielded satisfactory results, with an average coefficient of determination greater than 0.8. These numerical results imply that generating synthetic well-logging via deep learning can accurately estimate missing well-logging data, contributing to the reservoir characterization of gas-hydrate-bearing sediments. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

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14 pages, 4924 KiB

Open AccessArticle

Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM–FNN

by Hongtao Liu, Yan Jin, Xianzhi Song and Zhijun Pei

Appl. Sci. 2022, 12(15), 7731; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157731 - 01 Aug 2022

Cited by 7 | Viewed by 1732

Abstract

The drilling process is complex, especially for ultra-deep wells, which face the problems of high temperature, high pressure and poor drilling resistance in their formation. In order to establish an ROP (the Rate of Penetration) prediction model for ultra-deep wells, the characteristics of ultra-deep well drilling operations, such as formation temperature and formation pressure, are fully considered in the process of parameter optimization. Combined with the drilling mechanism and mutual information correlation coefficient, the final input parameters are determined. The powerful nonlinear fitting ability of the artificial intelligence method is very suitable for predicting the ROP. Considering the time sequence of multi-source data, this paper combines the powerful timing information-based mining ability of the LSTM (Long Short-Term Memory Neural Network) with the nonlinear fitting ability of FNN (Fully Connected Neural Network), and establishes an intelligent prediction model of the ROP based on a LSTM–FNN. The results show that the average relative error and

R^{2}

of the LSTM–FNN model on the data of well 1 and well 2 are better than the FNN and LSTM models. In addition, the accuracy of the LSTM–FNN model on the data of adjacent wells is reduced by only 5%, which further verifies the good mobility of the model. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

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13 pages, 3355 KiB

Open AccessArticle

A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling

by Zhaopeng Zhu, Xianzhi Song, Rui Zhang, Gensheng Li, Liang Han, Xiaoli Hu, Dayu Li, Donghan Yang and Furong Qin

Appl. Sci. 2022, 12(13), 6728; https://0-doi-org.brum.beds.ac.uk/10.3390/app12136728 - 02 Jul 2022

Cited by 7 | Viewed by 1943

Abstract

Managed pressure drilling (MPD) is an essential technology for safe and efficient drilling in deep high-temperature and high-pressure formations with narrow safety pressure windows. However, the complex conditions in deep wells make the mechanism of multiphase flow in drilling annulus complicated and increase the difficulty for accurate prediction of bottomhole pressure (BHP). Recently, an increasing volume of research shows that intelligent technology is an efficient means of accurately predicting BHP. However, few studies have focused on the temporal properties and variation mechanism of BHP. In this paper, hybrid neural network prediction models based on the multi-branch parallel are established by combining the different advantages of back propagation (BP), long short-term memory (LSTM), and a one-dimensional convolutional neural network (1DCNN) model. The results show that the relative error of the best model is about 70% lower than the optimal single intelligent model. Preliminary experimental results reveal that the hybrid models combine the advantages of different single models, which is more accurate and robust for extracting the temporal features of MWD. Finally, based on the trend analysis, the validity of the hybrid model is further verified. This study provides a reference for solving the problem of optimizing temporal characteristics and guidance for fine pressure control in complex formations. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

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25 pages, 15576 KiB

Open AccessArticle

Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning

by Hadyan Pratama and Abdul Halim Abdul Latiff

Appl. Sci. 2022, 12(13), 6723; https://0-doi-org.brum.beds.ac.uk/10.3390/app12136723 - 02 Jul 2022

Cited by 3 | Viewed by 2834

Abstract

A geological interpretation plays an important role to gain information about the structural and stratigraphic of hydrocarbon reservoirs. However, this is a time-consuming task due to the complexity and size of seismic data. We propose a semi-supervised learning technique to automatically and accurately delineate the geological features from 3D seismic data. To generate labeling data for training the supervised Convolutional Neural Network (CNN) model, we propose an efficient workflow based on unsupervised learning. This workflow utilized seismic attributes and KernelPCA to enhance the visualization of geological targets and clustering the features into binary classes using K-means approach. With this workflow, we are able to develop a data-driven model and reduce human subjectivity. We applied this technique in two cases with different geological settings. The synthetic data and the real seismic investigation from the A Field in the Malay Basin. From this application, we demonstrate that our CNN-based model is highly accurate and consistent with the previous manual interpretation in both cases. In addition to qualitatively evaluating the interpretations, we further extract the predicted result into a 3D geobody. This result could help the interpreter focus on tasks requiring human expertise and aid the model’s prediction in the next studies. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

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16 pages, 4021 KiB

Open AccessArticle

Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model

by Shuo Zhu, Xianzhi Song, Zhaopeng Zhu, Xuezhe Yao and Muchen Liu

Appl. Sci. 2022, 12(10), 5282; https://0-doi-org.brum.beds.ac.uk/10.3390/app12105282 - 23 May 2022

Cited by 12 | Viewed by 2734

Abstract

Stuck pipe phenomena can have disastrous effects on drilling performance, with outcomes that can range from time delays to loss of expensive machinery. In this work, we provide three methods for the prediction of stuck pipe. The first method targets the detection of friction coefficient which can represent the trend of stuck pipe. The second method targets the prediction of probability for stuck pipe using ANN (artificial neural network). The last model establishes a comprehensive indicator based on the first and the second method using fuzzy mathematics which can give more accurate probability for stuck pipe. The results show that the best model is the last one which can predict stuck pipe events with a F1 of 0.98 and a FAR (false alarm rate) of 1%. Preliminary experimental results on the available dataset indicate that the use of the proposed model and can help mitigate the stuck pipe issue. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

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16 pages, 7283 KiB

Open AccessArticle

Pay Zone Determination Using Enhanced Workflow and Neural Network

by Loris Alif Syahputra, Maman Hermana and Iftikhar Satti

Appl. Sci. 2022, 12(4), 2234; https://0-doi-org.brum.beds.ac.uk/10.3390/app12042234 - 21 Feb 2022

Cited by 1 | Viewed by 1897

Abstract

Amplitude versus offset (AVO) analysis and attributes are frequently utilized during the early stages of exploration when no well has been drilled. However, there are still some drawbacks to this method, including the fact that it involves a substantial amount of time and experience, as well as the subjectivity of manual analysis. By utilizing unsupervised learning, this process can be done more objectively and faster. Unsupervised learning can detect anomalies and identify patterns to understand more about the datasets since, at this early stage of exploration, there is still a lack of information and labelling. A type of unsupervised learning referred to as self-organizing maps (SOM) is applied in this study to delineate hydrocarbons from given AVO properties that were used to detect hydrocarbons. SOM is also used to eliminate redundancy in the selection of attributes prior to the delineation procedure. The investigation began with well log data and progressed ahead into multiple fluid conditions to evaluate the model’s ability to identify hydrocarbons. The analysis can then be extended to the seismic dataset. By combining SOM, correlation coefficient, and mean–median, a method is devised for filtering features to remove redundancy. On the hydrocarbon delineation process, the model managed to detect hydrocarbons using well log simulations and was confirmed using water saturation logs. Additionally, the model is validated using real seismic data, demonstrating a promising performance in defining probable hydrocarbons. The proposed method enables early detection of hydrocarbon content during the preliminary stage of exploration when no well is accessible. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Exploration and Production)

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