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13th International Field exploration and development Conference, IFEdC 2023

作者： Lin, Xia Xu, Chao Mi, Lan Liu, Zong-Shang Xiang, Chong Liu, Li-Xia PetroChina Research Institute of Petroleum Exploration and Development Beijing China Artificial Intelligence Technology R&D Center for Exploration and Development CNPC Beijing China

ISBN: (纸本)9789819702718

As an efficient and intelligent means of knowledge organization, knowledge graph has become the core force driving the development of artificial intelligence. Hydraulic fracturing is an important measure for increasing production and injection in oil and gas fields, with complex design processes and numerous influencing factors. In order to achieve rapid and accurate optimization of fracturing plan, this paper proposes a method for optimizing fracturing plan based on knowledge graph. By combing the system of fracturing domain knowledge, fracturing knowledge graph is constructed. Extracting characteristic parameters describing the geological engineering double sweet spot in multiple dimensions and multiple scales, and showing the characteristic parameter-related entities, relationships, and attributes as vectors via graph embedding technique. Integrate expert knowledge with artificial intelligence to build a fracturing effect prediction model and optimize the fracturing plan. In this study, more than 500 fracturing oil wells in a tight sandstone block are taken as objects to build a knowledge graph. Based on well test and production test data and historical production, this study predicts the fracturing stimulation effect and optimizes the fracturing engineering parameters. The calculation results indicate that factors such as reservoir thickness, oil saturation, number of fracture clusters, and half length of fractures have a significant impact on the fracturing effect. The coincidence rate between the predicted capacity of production and the actual capacity of production is over 91%, and the efficiency of fracturing plan design is increased by more than 20 times. The research results can provide scientific basis for predicting fracturing effects and optimizing fracturing engineering parameters, greatly improving the efficiency and quality of fracturing design, and improving the success rate of fracturing construction. © The Author(s), under exclusive license to Spr

关键词： Forecasting

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Multi-stage decoupling Preconditioning Solution technology for Large-Scale reservoir Simulation 13th

Multi-stage Decoupling Preconditioning Solution Technology f...

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13th International Field exploration and development Conference, IFEdC 2023

作者： Li, Ning Research Institute of Petroleum Exploration and Development PetroChina Beijing China Artificial Intelligence Technology R&D Center for Exploration and Development CNPC Beijing100083 China

ISBN: (纸本)9789819702633

In response to the complex seepage mechanism of unconventional oil and gas reservoirs, large-scale volume fracturing of multi-segment and multi-cluster horizontal wells, and differences in oil and gas pressure and saturation properties, which result in uneven distribution of eigenvalues, large value of conditions, and slow solving speed of nonlinear equation matrix, a multi-stage decoupling preconditioning solution technology was carried out on the Jacobian coefficient matrix of the oil and gas numerical discrete equation based on the implicit pressure explicit saturation method (IMPES) processing idea. Multi-grid method (AMG) + block based incomplete LU decomposition method (BILU0) are used to preprocess the Jacobian coefficient matrix in multiple stages, and the block based generalized minimum residual method (BGMrES) is used to solve the preconditioned nonlinear algebraic equations. The calculation result proves that compared with other single processing methods, this method effectively reduces the number of iterations for solving discrete equations while ensuring calculation accuracy, improves calculation speed, and has important application value for rapid formulation and decision-making optimization of oil and gas field development plans. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nonlinear equations

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Ontology Construction technology of Knowledge Graph in Oil and Gas exploration and development 13th

Ontology Construction Technology of Knowledge Graph in Oil a...

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13th International Field exploration and development Conference, IFEdC 2023

作者： Li, Ning ren, Liang Liu, Zong-Shang ren, Shu-Hang Xiang, Chong Wu, Bo-Yu Cai, Xuan Research Institute of Petroleum Exploration and Development PetroChina Beijing China Artificial Intelligence Technology R&D Center for Exploration and Development CNPC Beijing100083 China

ISBN: (纸本)9789819702718

With the gradual application of knowledge atlas technology in oil and gas exploration and development fields such as intelligent evaluation of oil and gas reservoir and intelligent identification of oil and gas reservoir through logging, the application of knowledge Atlas in oil and gas industry has attracted more and more attention. In view of the problem of knowledge expression and application in E&P sector field, a professional word bank of E&P was established based on web crawler technology, a feature corpus of E&P field was established by extracting E&P sector related achievement documents and literature data, and the principle of E&P knowledge graph ontology construction was formulated and the construction process was defined. The multistage classification system of oil and gas exploration and development knowledge atlas ontology is described and constructed from multiple dimensions such as sector, object and feature, and the knowledge atlas ontology model of oil and gas exploration and development sector domain is formed, which lays a foundation for the in-depth application of knowledge atlas technology in petroleum exploration and development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ontology

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A 3d attention U-Net network and its application in geological model parameterization

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Petroleum exploration and development 2023年第1期50卷 183-190页

作者： LI Xiaobo LI Xin YAN Lin ZHOU Tenghua LI Shunming WANG Jiqiang LI Xinhao AI Research Center Research Institute of Petroleum Exploration and DevelopmentPetroChinaBeijing 100083China Artificial Intelligence Technology R&D Center for Exploration and Development CNPCBeijing 100083China Department of Oilfield Development Research Institute of Petroleum Exploration and DevelopmentPetroChinaBeijing 100083China

To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3d attention U-Net network was proposed not using a trained C3d video motion analysis model to extract the style of a 3d model,and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this *** 3d attention U-Net network was applied to a complex river channel sandstone reservoir to test its *** results show that compared with CNN-PCA method,the 3d attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction,better reflect the fluid flow features in the original geologic model,and improve history matching results.

关键词： reservoir history matching geological model parameterization deep learning attention mechanism 3d U-Net

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Intelligent Prediction technology for Production of Tight Oil Based on data Analysis 13th

Intelligent Prediction Technology for Production of Tight Oi...

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13th International Field exploration and development Conference, IFEdC 2023

作者： Li, Ning Wu, Xiang-Hong Li, Xin Wang, Zhi-Ping Wang, Yue-Zhong Zhao, Li-Ao ren, Liang Wang, Hong-Liang Tian, Hong-Yu ren, Shu-Hang Jiang, Si-rui Research Center of Artificial Intelligence Research Institute of Petroleum Exploration and Development PetroChina Beijing China Artificial Intelligence Technology R&D Center for Exploration and Development CNPC Beijing100083 China Department of Asia-Pacific E&P Research Institute of Petroleum Exploration and Development PetroChina Beijing China

ISBN: (纸本)9789819702718

China is rich in tight oil resources, with a wide distribution range and a large amount of resources, making it one of the key areas for strategic replacement of future oil reserves and production. In response to issues such as strong heterogeneity of terrestrial tight oil reservoirs, difficulty in drilling high-quality oil layers, large production differences, and unclear main control factors for production capacity, a detailed analysis of dynamic and static data of production wells was conducted to analyze production performance and decline patterns. Production wells were classified according to production characteristics, and development indicators at different stages were statistically analyzed based on actual production days. Using a combination of principal component analysis and Pearson correlation coefficient, based on multiple dynamic and static data such as geological factors, fracturing factors, and development factors, and analyzing the correlation between different single and combined factors and cumulative oil production at different stages, the main control factors for different production stages of tight oil were obtained. A production capacity prediction model for tight oil fracturing horizontal wells was established based on machine learning intelligent algorithms, A production capacity evaluation and prediction technology for tight oil fracturing horizontal wells has been developed. By comparing with actual production data, the accuracy of the predicted results can meet production needs, providing a strong technical foundation for precise prediction and guidance of tight oil production in China. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Quality control

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Fracture Characterisation Methods and Application of Unconventional Oil and Gas reservoirs in reservoir Numerical Simulation

Fracture Characterisation Methods and Application of Unconve...

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2023 SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition, APOG 2023

作者： Li, Qiaoyun Wu, Shuhong Cao, Zijian Wang, Baohua Li, Hua Jia, Han RIPED PetroChina China Artificial Intelligence Technology R&D Center for Exploration and Development CNPC China

ISBN: (纸本)9781613999813

It's necessary to solve the problem of fine characterization and efficient simulation of three types of fractures, i.e. natural fractures, artificial fractures and complex fracture network, for the development optimization design and scheme adjustment of unconventional oil and gas reservoirs. This paper describes a set of efficient and practical fracture characterization techniques for reservoir numerical model, which integrates various numerical simulation methods of fractures in different scales, including single-medium equivalent model, LGr model, double-medium model, MINC model, non-structural discrete fracture model and embedded discrete fracture model. For different reservoir types and different fracture dimensions, different models are adopted to depict the spatial distribution and fluid flow characteristics of fracture-matrix, matrix-matrix and fracture-fracture. The application in a shale gas shows that the set of fracture characterization methods can accurately describe the shape, geometric characteristics and spatial position of natural fractures, artificial fractures and fracture network. The methods also can simulate the fluid flow characteristics in multi-scale fracture network efficiently, and accurately predict the productivity of fracture network. Copyright 2023, Society of Petroleum Engineers.

关键词： Natural fractures

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Factor Analysis of Affecting the Accuracy for Intelligent Picking of Seismic First Arrivals with deep Learning Model 12th

Factor Analysis of Affecting the Accuracy for Intelligent Pi...

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12th International Field exploration and development Conference, IFEdC 2022

作者： Li, Ning Gong, ren-Bin Li, Xin Li, Wei-Wei Wu, Bo-Yu ren, Shu-Hang Research Institute of Petroleum Exploration and Development PetroChina Beijing China Artificial Intelligence Technology R & D Center for Exploration and Development CNPC Beijing100083 China

ISBN: (纸本)9789819919635

Picking up the Seismic first-arrival wave has always been a basic and important thing in seismic data processing. However, in the case of large amount of data and low signal-to-noise ratio, picking the first-arrival wave entirely by hand has the following problems: the efficiency is low, and it is difficult to meet the requirements of timeliness in production. Based on the U-Net convolutional neural network, a supervised learning model and a transfer learning model for first-arrival wave picking were established, and a supervised learning and transfer learning sample library for first-arrival wave picking suitable for the experimental work area was constructed. Supervised learning and transfer learning model training were carried out through hyperparameter tuning, and the model prediction results were evaluated according to the error of statistical prediction result of the model test set data. It has been proved by oilfield application that the supervised learning model, transfer learning model and intelligent picking method can meet the actual needs of the oilfield in terms of the prediction accuracy and picking efficiency of the first-arrival wave picking of the 3d seismic data in the work area, and have the value of widespread application in oil and gas fields. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Convolution

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Study on the Multiphase Flow Behavior in the Jet Pump Lifted Natural Gas Hydrate Wells with Combined depressurization and Heat Injection Method

Study on the Multiphase Flow Behavior in the Jet Pump Lifted...

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International Petroleum and Petrochemical technology Conference 2024, IPPTC 2024

作者： Ping, Xiaolin Han, Guoqing Zhang, Jiqun Wang, Hongliang Liu, Changchun Liu, Jungang China Research Institute of Petroleum Exploration & Development PetroChina China Artificial Intelligence Technology R&D Center for Exploration and Development CNPC China PetroChina Changqing Oilfield Company China Exploration & Development Research Institute of Changqing Oilfield China

Natural gas hydrate production tests have been carried out successfully with different exploitation methods and gas recovery drainage technologies. The purpose of this study is to elucidate the fluid flow behavior in the jet pump lifted natural gas hydrate wells and optimize hot water injection parameters for the jet pump drainage gas recovery technology. The mechanism of jet pump drainage recovery of natural gas hydrate wells was analyzed and its applicability to natural gas hydrate exploitation with combined depressurization and heat injection method was demonstrated. In addition, multiphase flow models of tubing and annulus were established respectively for the phenomenon of countercurrent flow of heat exchange in the jet pump drainage gas recovery process, and the corresponding multiphase flow laws were derived. Based on the study of multiphase flow, sensitivity analysis and optimization of heat injection parameters were performed. It has been demonstrated that jet pump drainage gas recovery technology is suitable for the exploitation of onshore natural gas hydrate with combined depressurization and heat injection method. The multihphase flow behavior in the tubing and annulus of the jet pump lifted natural gas hydrate wells was disclosed in this study. Numerical simulation results show that the temperature and pressure distribution in the wellbore of the jet pump lifted natural gas hydrate wells during the recovery process are affected by the injection conditions. This study provides theoretical guidance for the production optimization and hydrate prevention and control in the wellbore of the jet pump lifted natural gas hydrate wells. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Sensitivity analysis

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Carbonate Fracture-Cavity reservoirs Prediction technology Based deep Learning Model 13th

Carbonate Fracture-Cavity Reservoirs Prediction Technology B...

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13th International Field exploration and development Conference, IFEdC 2023

作者： Li, Ning Gong, ren-Bin ren, Liang ren, Shu-Hang Sun, Jiang- tao Yu, Xiao Gan, Chun-Ting Research Institute of Petroleum Exploration and Development PetroChina Beijing China Artificial Intelligence Technology R&D Center for Exploration and Development CNPC Beijing100083 China Technology Consult Center Research Institute of Petroleum Exploration and Development PetroChina Beijing100083 China Huawei Technology Co. Ltd. Beijing100085 China

ISBN: (纸本)9789819702718

Paleozoic carbonate rock is the key field of oil-gas exploration in Tarim Basin. Fracture-cavity reservoirs are often developed in carbonate reservoirs in Tarim Oilfield, a large amount of oil and gas resources are dis-tributed in Paleozoic reservoirs with different burial depths and scales, accurately and quickly identifying these fracture-cavity reservoirs is Significant to the oil and gas exploration, development, and production of the Tarim Oilfield. With the continuous development of artificial intelligence technology, machine learning methods have been widely applied in various scenarios of oil and gas exploration and development, bringing new opportunities for the development of carbonate reservoir prediction technology. Based on drilling, logging and seismic data, this study comprehensively analyzes the structure, rock and physical properties of carbonate reservoirs in the study area, exploring the main controlling factors of carbonate reservoirs. On this basis, a sample set corresponding to the fracture-cavity reservoirs in the study area was constructed, by using machine learning methods, a prediction model for carbonate rock fracture-cavity reservoirs has been established, which can intelligently predict carbonate rock fracture and cave reservoirs in the research area. The trained model of carbonate rock fracture and cave reservoir prediction can quickly and accurately identify fracture and cave reservoirs on post stack seismic data. This study demonstrates that methods are based on machine learning can quickly and efficiently predict carbonate rock fracture-cavity reservoirs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Assessment of different Field development Strategies deploying Maximum reservoir Contacting (MrC) Well in Carbonate reservoir: A case study of Oilfield r

Assessment of Different Field Development Strategies Deployi...

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2023 SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition, APOG 2023

作者： Jia, Han Tong, Min Peng, Hui deng, Xili Song, ruixuan Wang, rui RIPED PetroChina China Artificial Intelligence Technology R&D Center for Exploration and Development CNPC China University of California Santa Cruz United States

ISBN: (纸本)9781613999813

deploying horizontal well is one of the efficient methods to enhanced oil recovery. Maximum reservoir Contact (MrC) well, based on the horizontal well, which indicates the contacting area of wellbore and reservoir should be as large as possible to obtain the optimum exploration effects. The purpose of this paper is to build an assessment and optimization work-flow, which guides the well pattern improvement and well-type selection via a case study of the filed r in a Middle East Carbonate reservoir. Analyzing several field development index, like production performance, gas oil ratio, water cut, pressure, etc. in a pilot-scale well pattern both for the conventional horizontal wells and MrC wells using different injection schemes, including injection timing, injection volume, and injection components, via reservoir numerical simulation. The method using to evaluating reservoir exploration effect in this paper is numerical simulation, assuming different development strategies and well-type, via reservoir numerical simulator. Control-variate method as well as used in this study, which focus on keeping single factor analysis for the simulation. Comparing all the field development indexes among different cases while keeping other factors consistent, for example, exploring the influence from well types, which are MrC well and conventional horizontal well, two cases must be deployed the same injection schemes. Simulating different cases with analyzing the results, MrC wells obtain the better oil production generally compared with conventional horizontal wells. However, oil production performance is not the only index which is cared for in oil & gas industry, additionally, in terms of reservoir engineering, water cut, water breakthrough, gas breakthrough, gas oil ratio, oil steady production plateau, etc., while some economic indexes are extremely concerned by the experts and companies. Consequently, assessment of MrC wells effect should consider many factors from the perspe

关键词： Gases

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