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AI-based geo-engineering integration in unconventional oil and gas
⁎Corresponding author at: 18 Fuxue Road, Changping District, Beijing, China
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Received: ,
Accepted: ,
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.
Peer review under responsibility of King Saud University.
Abstract
In the oil and natural gas industry, artificial intelligence (AI) technology has penetrated into all links from exploration and development to construction sites. This paper investigates the application of artificial intelligence in each link of oil reservoir. It is found in investigation that each algorithm plays a different and important role at each stage and every link of oil accumulation development cannot leave the cooperation of artificial intelligence. But, the application of AI is mostly scattered, forming a physical isolation, a lot of single information-island. This situation increases the communication cost of cross-departmental data cooperation and the repetitive screening and recognition work has seriously affected work efficiency. To address unsolved problems with the current application of AI, AI-based geo-engineering integration in unconventional oil and gas are proposed this article considers. Integrate data islands, and realize internal resource sharing, treat exploration and development as an organic whole, extend exploration to development. This article takes the well factory operation mode, meanwhile, the real-time synchronization and coordination of all links has been fully realized. This kind of integration of geology and engineering is helpful to realize coordination and cooperation at all levels, regions, and disciplines, effectively benefiting development of unconventional oil and gas reservoirs.
Keywords
Artificial intelligence algorithm
Unconventional reservoir
Engineering integration
Geo-engineering integration
Exploration and development
1 Introduction
AI began in the 1960s and enjoyed a notable success. AI provides new ideas to unconventional oil and gas development (Li et al., 2020; Yang and Zou, 2019). Liu et al. (2018) use hybrid intelligent systems (HIS) to optimize the development effect is the main application method of AI in the field of reservoir development and development. AI also provides a more accurate method for selecting the level of stimulation measures. Xinjiang Oilfield Company adopted BP-NN and LM algorithms, and used GR to optimize the construction plan, and realized the scientific decision-making and fine optimization of the parameters of the fracturing plan (Maity and Ciezobka, 2019). Al-Fattah developed a rigorous and advanced data-driven model called GANATS which can describe and predict global crude oil demand. Al-Fattah (2021). After oil and gas reservoirs are exploited, crude oil transportation and preservation are key issues. An intelligent simulation decision-making system for crude oil storage and transportation scheduling is designed based on the C++ Builder 6 program development platform (Yuan, 2018). Ahmadi et al. (2012) developed a unified particle swarm optimization method based on neural networks to predict asphaltene precipitation, which method is based on ANN to predict asphaltene qualitative precipitation. AI plays an important role in oil and gas storage applications ( Koroteev and Tekic, 2021; Kuang et al., 2021; Maryam and Abbas, 2018).
However, the above methods each form a system, there is currently no systematic summary of AI in the integration of geological engineering in unconventional oil and gas (Xie et al., 2017; Chen et al., 2021). This study analyzes AI in the construction of reservoir geo-engineering and the cases of AI-based integrated work platforms. Finally, the future research directions and suggestions for the integration of artificial intelligence in unconventional oil and gas geological engineering are put forward. It provides a reference for the application of artificial intelligence integration in unconventional oil reservoirs.
2 The trend of geo-engineering integration
The status of unconventional oil and gas in global energy supply has become increasingly prominent and has become an important part of oil and gas production, reshaping the global energy map and pattern (Jia, 2017; Tong et al., 2018; Zou et al., 2018).
Unconventional oil and gas, taking tight oil with higher heat as an example, strong heterogeneity, poor seepage ability, low single well production, low oil and gas field recovery rate and unstable production are also among its characteristics (Jiao, 2019; Yang and Zou, 2019; Sun, 2019). In the traditional oilfield management model, the continuous process of geology and engineering is artificially divided, and there is a lack of inheritance and reciprocating correction process between the data. If the construction result of the back-end project cannot be fed back to the geology department in time, the reservoir model cannot be revised in time (Yang et al., 2020).
Taking China as an example, all major oil fields throughout China seeks to become new kinds of oil fields under the integrated management model of geo-engineering. It successfully established and realized the “ integration of exploration and development deployment, integration of exploration and development management, integration of exploration and development research, integration of reserves and production. In the specific work, the integration of production and scientific research, the integration of engineering and geology, and the integration of oil testing with development” is the overall framework of the main content (Li et al., 2020; Redutskiy, 2017; Wan et al., 2020; Yao, 2014).
3 A powerful tool-AI
Artificial intelligence (AI) refers to intelligently developed machines that is programed to learn, think, perceive, and solve problems like humans. With the leap in computer processing capabilities and the continuous innovation of algorithms, AI technology has achieved unprecedented development and penetrated into all aspects of life. In the energy sector, AI is indispensable as well.
3.1 Why supportive?
Artificial intelligence (AI) refers to intelligently developed machines that is programed to learn, think, perceive, and solve problems like humans. AI algorithms are often used in oil reservoirs. Each algorithm has its own principle.
ANN is the ability to model complex non-linear processes without the need to establish any relationship between input and output variables. Fuzzy Logic can use “human language” to describe problems and their fuzzy solutions. GR is simple chromosome-like data structures that encode potential solutions to specific problems. The principle of SVM is based on statistical learning theory and structural minimization. Case-based reasoning (CBR) is to reuse one or more similar cases that have been previously solved in new problems.
The efficient application of AI requires combined use of algorithms for optimization purposes. Meanwhile each question is unique, and model developed needs to consider the nature of the problem.
3.2 Summary of commonly used AI technologies in the oil and gas sector
In order to understand the characteristics of each algorithm in depth and provide a basis for algorithm optimization in AI oilfield exploration and development, the characteristics of some widely used algorithms in the field are summarized. The application of artificial intelligence in the reservoir can be classified as follows according to the algorithm.
ANN are used for reservoir characterization, optimization design of production stimulation measures, drill bit selection, and optimization of field operations. Fuzzy logic has been used reservoir characterization, production enhancement measures, enhanced oil recovery, and decision analysis. Genetic algorithms is used for real-time optimization of the entire oil and gas production process. Support vector machine methods commonly used in oilfields are used to identify flooded layers. The literature shows that drilling plans can be optimized and executed through CBR.
However, it is worth noting that in practical applications, AI may have defects such as difficulty in reaching the optimal solution, overfitting, and large demand for computing capacity.
3.3 The influences of AI on the petroleum industry and oil and gas economic design
The influences of AI on the economic design of the oil and gas industry is reflected by the selection of one or more models for comparison and correction through value assessment. For example, what accounts for the increase in costs? Through the analysis of the single well data with the big data of artificial intelligence in each oil field, it can be understood that the increase of relevant costs is directly related to the actual production. At the same time, the artificial intelligence system is used to simulate the impact of each oil field's production capacity construction on the ROI, and finally the reasonable economic design is obtained.
The design of oil well and petroleum industry by artificial intelligence is mainly embodied in the overall optimization of new and old data. Through the value evaluation, the benefit index of each scheme is determined to provide scientific basis for the final optimization decision. According to the specific situation, one or more modes can be selected for mutual comparison and correction.
4 AI and geo-engineering integration
In the concept of unconventional oil and gas reservoir geological engineering integration, “geology” and “engineering” are not synonymous with disciplines in a narrow sense. “Geology” broadly refers to the comprehensive multi-disciplinary research work centered on reservoirs, including reservoir characterization, geological modeling, geomechanics, and reservoir engineering evaluation. “Engineering” refers to a series of engineering technologies from drilling to production in the process of exploration and development, including drilling, fracturing, oil production, reservoirs, economic evaluation, etc. Under the traditional oilfield management model, geological and engineering data are physically and logically separated. Having separately stored data, data are analyzed independently by different departments. It results in low communication efficiency and duplication of work (Fu et al., 2019; Li et al., 2019; Shu et al., 2020).
The integration of geo-engineering (Fig. 1) requires interaction between geology and engineering, which provides a basis for engineering operations such as drilling and fracturing. As the progress of engineering operations changes, timely updating of new data also helps to update the geological model in real time. Because each link of the exploration and development of unconventional oil and gas fields involves a large amount of heterogeneous data, the massive data processing advantage of AI brings opportunities for real-time communication and collaborative processing of all links of geological engineering.
The integration of gen-engineering.
4.1 Application of AI in reservoir geology
Reservoir-centric geological modeling and reservoir characterization are the basis for engineering construction. With the technological progress of exploration and development, the foundation of geological data, application scenarios and application purposes are constantly undergoing profound changes. Table 1 summarizes part of the AI technology application research involved in reservoir geology.
Geological link
method
Author
Application
Lithology identification
CNN
(Chen et al., 2020; Wu et al., 2021)
Characterization of tight reservoirs, low resistivity pay identification
INN
Kulga et al. (2018)
FSVM
Cao et al. (2018)
SVM + ANN
Han et al. (2017)
SVM + BP
Ge et al. (2019)
Reservoir evaluation
SVM + ANN + RBF HIS
Duan et al. (2020)
Reservoir physical parameters Reservoir physical parameters
PSO + SVM
Yin (2016)
Hybridgenetic algorithm
Yin et al. (2017)
Crack identification
A NN
(Ahmed et al., 2019; Jing et al., 2020; Nanda et al., 2019)
Crack pressure, closure pressure
ANN + Fuzzy
Fan (2016)
Three-dimensional crack distribution
CNN
Wu et al. (2018)
Fractured reservoir category
ML + DBA + S VM
Anderson et al. (2016)
Automatically identify cracks
BP
Huang et al. (2016)
Crack density
Generally speaking, artificial neural network algorithm is one of the most widely used algorithms in the geological link of unconventional oil reservoirs. The research ideas of ensemble learning, especially selective ensemble learning, are gradually used by researchers (Li et al., 2021). At present, learners integrated with different intelligent algorithms have been used to classify lithology. Learners based on lithology classification are used to further identify the physical properties of the reservoir and combine the geological information to determine the weight of each intelligent method in the learner (Fig. 2).
Learners integrated with different intelligent algorithms.
It is evident that scholars nowadays are no longer limited to the application of traditional methods, but are constantly trying new methods, seeking more general knowledge representation and reasoning algorithms to achieve better application effects. There are still deficiencies in geological evaluation based on artificial intelligence, and the classification accuracy and scope of application need to be further improved. Algorithm optimization and optimization are still the direction of AI research. At the same time, this selection and integration approach is applicable to all aspects of geological engineering and has great application prospects.
In the research of reservoir geology, AI is not only applied to a single technical link, but also gradually applied to the construction of a geological intelligence platform, which partially reflects the concept of integration. The application of AI in logging data interpretation and other links is the technical preparation and foundation for the ultimate realization of geo-engineering integration.
In order to enable the sharing of reliable geological information obtained by AI in the geological process, and to improve the information management level and data processing efficiency of oilfields, a geological analysis data intelligent platform came into being as the technical foundation of geo-engineering integration (Zhao et al., 2018) (Fig. 3).
Geological analysis data intelligent platform.
The platform is a basic preparation for the integration of geology and engineering. Its idea is to use artificial neural networks (ANN), BP neural network methods, support vector methods, genetic algorithms and other artificial intelligence methods to display seismic interpretation results, structural models, and three-dimensional visualization of attribute models, dynamic models, dynamic production profiles, etc. Moreover, the platform can correlate the change rules between data, predict development indicators, evaluate the effects of measures, adjust process parameters, tap the potential of reservoir production, achieving the goal of data-driven decision-making in oil fields.
4.2 AI applications in engineering
Due to many production problems in the development of oil fields, engineering technology optimization is imminent. Engineering optimization can improve technology, increase capital returns and ensure the stability of manpower. With the help of artificial intelligence analysis and optimization engineering theory and method, to promote the intelligent transformation and upgrading of petroleum industry.
The “ engineering ” link is to screen, optimize and guide the implementation of a series of drilling and production, as well as to assess solutions. It makes continuous adjustment and improvement of engineering technical solutions, enriching knowledge and engineering operation from drilling to fracturing in a systematic way. This paper focuses on the application areas of different AI algorithms in drilling, fracturing, and productivity evaluation (Table 2).
Engineering link
method
application
Drilling
ANN (Gidh et al., 2012)
Drilling plan optimization, drill bit optimization, wellbore stability, construction optimization
BP + GA (Opeyemi, 2016)
Drilling platform selection, stuck control and corrective measures
CBR (Tiago et al., 2012)
Real-time risk monitoring and decision-making
Wellpattern design
QGA
Layout and optimization of well patterns
ANN (Saputelli et al., 2002)
PSO (Mellit et al., 2009)
Preferred measures
GA
Well spacing involves
fracture
BP
Optimal fracturing parameters,
BP + GA
Fracturing effect prediction
ANN (Saputelli et al., 2002; Wei et al., 2020)
Fracturing effect
test
CNN (Al-Kaabi et al., 1993; Juniardi et al., 1993)
Wellbore dynamic liquid level recognition
Capacity evaluation
ANN BP(Mohaghegh et al., 1994)
Water saturation, flow law, reservoir dynamic analysis capacity forecast
FCM + SVM (Anifowose et al., 2011; Shi et al., 2018)
Reservoir dynamic analysis (water content)
GNN + IPSO (Ahmadi, 2012)
Capacity forecast
SVM (Ahmadi et al., 2013; Ahmadi, 2015)
ANN + FCM (Sebakhy and Emad, 2009)
PCA + APSO + LSSVM (Ahmadi, 2015)
Production management
Among them, drilling is an important construction link of the project, and its construction cost is high. The selection of equipment and plans before drilling, and the real-time monitoring of construction during drilling will affect the drilling effect. A large number of factors that need to be considered in the prediction of each link. Complex situations, and unpredictable construction conditions all set barriers for drilling. AI has been successfully applied in various aspects of drilling design, construction, and testing, which has improved the adaptability of the drilling process to complex situations.
Fracturing is the main measure and means to increase production of oil and gas reservoirs, and high-efficiency fracturing is of great significance for improving the recovery of unconventional oil and gas. To improve the effect and efficiency of fracture prediction and optimize fracturing plan, it is necessary to introducing AI and machine learning methods to analyze the geological and engineering process parameters that affect the fracturing effect.
In the engineering process, it is also necessary to establish a collaborative engineering decision-making and construction platform to realize the integrated technology construction of fracturing, drilling and productivity evaluation, in order to achieve integration and factorization, combined with the summary in Table 2.
4.3 Application case analysis
Although the idea of integration has been reflected in the two links of geology and engineering, the true realization of AI geo-engineering integration still requires further interaction and integration of the two links. Currently, practitioners around the world are constantly conducting integrated exploration of unconventional oil and gas. In Sichuan China, Fuling marine shale gas, Dagang Oilfield and other complex oilfields have combined their own characteristics and made continuous efforts to integrate development compatible with their own geological and production characteristics. For the integration of geo-engineering in unconventional oil and gas, further promotion of chemistry has certain practical significance. In the meantime, Green field Heavy Oil Asset in Kuwait 's data integration management ideas in the oilfield planning and construction process have seen more gratifying applications. Below, the application cases are analyzed to gain experience and explore future research directions (Redutskiy, 2017; Wen et al., 2018; Liu, 2017; Ling et al., 2010; Salaheldin, 2018).
4.3.1 Western South China Sea Oilfield
The integration of AI has achieved remarkable application effect in oil and gas production (Shen, 2018; Zhang and Zhu, 2013). Based on Western South China Sea Oilfield, the research and development of integrated functions such as test design, real-time optimization and adjustment, post-test productivity analysis and well test interpretation are carried out.
Conventional methods are not suitable for complex reservoirs in Western South China Sea Oilfield, the opening/closing time and production system allocation are unreasonable, and there is no combination of pre-drilling prediction and post-drilling correction. In response to the above problems, the study proposed the following four steps.
-
(1)
Establish the prediction model of reservoir, fluid physical property and productivity parameters.
-
(2)
Study on test design method based on parameter prediction.
-
(3)
Research on real-time optimization and adjustment method of test working system.
-
(4)
Development of well test design and real-time optimization decision system.
Based on the above 4 steps, the test experience of domestic and foreign oil fields is used for reference by this research. Big data ideas were applied, including reservoir and fluid property parameter prediction modules were established. The test plan is designed and the module is adjusted in real time. The productivity analysis and well test interpretation module and the knowledge generation and management module are also included in the R&D software system. Use the mature rules of the developed blocks to predict the physical properties of reservoirs and fluids, and design a reasonable testing system (Fig. 4). According to the actual measurement dynamics on site, real-time adjustment of the work system, production capacity forecast analysis.
Geo-engineering integration in Western South China Sea Oilfield.
This research can flexibly generate the parameter prediction model, construct the knowledge base for parameter prediction, design the test scheme, determine the reasonable test process, collect the real-time data during the test and optimize the test working system.
4.3.2 Dagang complex fault block oil field
Dagang Oilfield is a typical complex fault block oilfield. Geo-engineering integration in Dagang Oilfield (Zhao et al., 2018) (Fig. 5), the degree of waterflooding control increased from 63.5% to 80.1%, the degree of injection-production connectivity increased from52.2% to 81.7%, and the injection-production response rate increased from 65.5% to 80.2%. The labor productivity has increased by 30%, and the system saves energy and reduces consumption by more than 10%,provide practical guidance for the construction of integration of geology and engineering and enable integration from concept to reality.
Geo-engineering integration in Dagang Oilfield.
In terms of AI application in each link, ANN and fuzzy logic are applied to reservoir characterization. The quantitative characterization accuracy reaches 0.2 m. The application of fuzzy logic comprehensively considers the development and distribution of interlayers, the degree of reservoir production, the degree of water flooding, the adaptability of the split injection process, and the needs of tertiary production. The method is applied to the decision-making analysis. In the engineering application, the support vector method is used to diagnose the working condition of the pumping unit. The drilling is optimized and executed through CBR Plan and PSO develop new wells and drilling simulators, combining parameter integration schemes to optimize on-site development. ANN is used to select drill bits and optimize on-site operations.
4.3.3 Marine shale gas in southern China (Fuling shale gas)
With long-term unremitting efforts, the Fuling shale gas field has become the world's largest shale gas field outside of North America. Through technological innovation, integrated innovation and management innovation, the integrated development model is actively implemented. The entire working process and ideas are based on exploration and development, surface and underground, scientific research and production integrated research and on-site implementation, achieving high-level, high-speed, high-quality, and high-efficiency development (Xiao et al., 2018) (Fig. 6).
Geo-engineering integration in Fuling Shale Gas.
In combine with the concept of geo-engineering integration, the intelligent geological engineering platform is used to identify address before fracturing, and the fracturing is performed in combination with different geological conditions. Fracture construction design, refined zoning and refined design are combined with each other. During the fracturing construction, through the joint display of the real-time occurrence of micro-seismic events and the dynamic changes of the construction curve, the construction situation, the formation situation and the crack characteristics are analyzed and judged. The next construction measures are adjusted to ensure the construction Safe and smooth. Geological model is updated via the implementation of monitoring fracturing effect and real-time adjustment and post-fracturing evaluation and analysis (applying PSO and CBR). On this basis, use virtual well technology to perform drilling and layout optimization on subsequent development wells, and evaluate potential geological engineering challenges and risks.
The integrated application of geo-engineering in Fuling shale gas fully integrates engineering and geology, providing reference for other unconventional oil and gas resource development in China.
4.3.4 Green field heavy oil asset in Kuwait
Green field is an attempt by young oil fields to integrate geo-engineering data management. Green field Heavy Oil Asset in Kuwait uses data integration and visualization technology for Well, Reservoir and Facility Management (WRFM) (Kharghoria et al., 2019). It is still in the early stage of construction. The data volume is relatively small in comparison to mature and old oil fields, but data integration in the practice of this oil field shows a clearer application prospect.
In accordance with the above three cases, it is clear that the data mining and interaction potential of AI has not been fully utilized in terms of communication management and decision-making in the construction of integration of geo-engineering. The integration is mostly reflected in new wells, new blocks, and new oil fields. Even in old oil fields such as Dagang Oil Field, production is increased through the construction of new areas. The improvement and application of old well and old data remains problematic. Furthermore, there is lack of insightful and deep analysis on the impact of the real-time update of engineering data on geological data. Although there is work involving the matching of production parameters and geological parameters, the research on the relationships and the corresponding decision-making mechanism needs to be further strengthened. Therefore, in the work of unconventional oil and gas geo-engineering integration can be combined with this phenomenon to explore the integration of old well areas, old data and new data, and new technologies, ultimately achieving a more comprehensive real-time shared database and algorithm library.
5 Conclusions
The application cases suggest the realization of geo-engineering integration to some extent. However, the challenges faced still require further assistance from high-tech such as AI. Based on summary work and case analysis, findings are summarized as follows:
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(1)
Taking the well factory operation mode as an example, it is of certain significance when combining historical successful cases, establishing a standardized case model, forming a reference sample library, and combining CBR and other technologies for program design.
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(2)
The comprehensive algorithm has higher accuracy and better effect than a single algorithm. The establishment of corresponding algorithm libraries for each link and the realization of different algorithm optimization for different reservoir structures are one of the potential directions of geological engineering algorithm research.
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(3)
The unification of the formats of old data and new data, geological data and engineering data is still one of the challenges faced in effective database construction. The realization of data transformation and big data integration is the key to complete geo-engineering integration.
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(4)
The previous artificial intelligence is very effective in every aspect, but when each sector is applied to each other, because each department is independent, the application or query of data will be delayed. The integration of geology and engineering enables internal resource sharing, treating exploration and development as an organic whole, which accelerates the implementation process of exploration and development, and shorten the conversion of resources to reserves, reduces the time for reserves to production conversion, and saves funds spent on exploration and development and oilfield construction.
Funding
The authors are grateful for financial support from the National Natural Science Foundation of China (11872073) and the National Science and Technology Major Projects of China (Grant Nos. 2017ZX05009-003).
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References
- Neural network based unified particle swarm optimization for prediction of asphaltene precipitation. Fluid Phase Equilib.. 2012;314:46-51.
- [Google Scholar]
- Connectionist approach estimates gaseoil relative simulation in petroleum reservoirs: application to reservoir simulation. Fuel. 2015;140:429-439.
- [Google Scholar]
- Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl. Soft Comput.. 2013;13(2):1085-1098.
- [Google Scholar]
- New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. Fuel.. 2012;102:716-723.
- [Google Scholar]
- New artificial neural networks model for predicting rate of penetration in deep shale formation. Sustainability-Basel. 2019;11:6527-6544.
- [Google Scholar]
- Application of the artificial intelligence gannats model in forecasting crude oil demand for saudi arabia and china. J. Petrol. Sci. Eng.. 2021;200:108368.
- [CrossRef] [Google Scholar]
- Using Artificial Neural Networks To Identify the Well Test Interpretation Model. Formation Evaluation, 26-28 September. Society of Petroleum Engineers, USA, pp 1993:233-240.
- [Google Scholar]
- Using machine learning to identify the highest wet gas producing mix of hydraulic fracture classes and technology improvements in the Marcellus shale. In: Unconventional Resources Technology Conference, 1-3 August. San Antonio, Texas, USA. 2016. p. :3-11.
- [Google Scholar]
- Prediction of Oil and Gas Reservoir Properties using Support Vector Machines. International Petroleum Technology Conference,15–17 November . 2011
- [CrossRef] [Google Scholar]
- Application of permeability interpretation based on fuzzy support vector machine in sandy conglomerate reservoir. J. Xinjiang Univ. (Nat. Sci. Ed.).. 2018;35:264-271.
- [Google Scholar]
- A new method of lithology classification based on convolutional neural network algorithm by utilizing drilling string vibration data. Energies. 2020;13(4):888.
- [CrossRef] [Google Scholar]
- Geology-engineering integration key technologies for ten billion cubic meters of shale gas productivity construction in the Southern Sichuan Basin. Nat. Gas. Ind.. 2021;41:72-82.
- [Google Scholar]
- Artificial intelligence in oil and gas upstream: trends, challenges, and scenarios for the future. Energy and AI 2021
- [CrossRef] [Google Scholar]
- Application of selective ensemble learning model in lithology-porosity prediction. Sci. Technol. Eng.. 2020;20(03):1001-1008.
- [Google Scholar]
- Evaluation and prediction of fractures in volcanic reservoir of Huangshatuo area. Jilin Geol.. 2016;35:16-20.
- [Google Scholar]
- New progresses, prospects and countermeasures of natural gas exploration in the Ordos Basin. China Petrol. Explor.. 2019;24:418-430.
- [Google Scholar]
- Tectonic discrimination based on convolution neural network and big data of volcanic rocks. Earth Sci. Front.. 2019;26:22-32.
- [Google Scholar]
- Artificial neural network drilling parameter optimization system improves ROP by predicting/managing bit wear. In: Intelligent Energy International, 27-29 March. Society of Petroleum Engineers, Utrecht, The Netherlands. 2012. p. :2-13.
- [Google Scholar]
- Selection of model variables for pattern recognition methods and its application in low resistivity pay identification. Well Logging Technol.. 2017;41:171-175.
- [Google Scholar]
- The Application of BP neural network to DMT hill metamorphic fracture prediction. J. Eng. Geophys.. 2016;213:483-490.
- [Google Scholar]
- Breakthrough and significance of unconventional oil and gas to classical petroleum geology theory. Pet. Explor. Dev.. 2017;44(1):1-10.
- [Google Scholar]
- Re-recognition of “unconventional” in unconventional oil and gas. Pet. Explor. Dev.. 2019;46(5):847-855.
- [Google Scholar]
- Artificial neural network–based time-domain interwell tracer testing for ultralow-permeability fractured reservoirs. J. Petrol. Sci. Eng.. 2020;195:107558.
- [CrossRef] [Google Scholar]
- Complexities of Using Neural Network in Well Test Analysis of Faulted Reservoirs. Western Regional Meeting, 26-28 May. Society of Petroleum Engineers, Anchorage, Alaska, pp. 1993:20-31.
- [Google Scholar]
- Application of big data techniques in a greenfield heavy oil asset in Kuwait – a feasibility study. In: Kuwait Oil & Gas Show and Conference, 13-16 October. Society of Petroleum Engineers, Mishref, Kuwait. 2019. p. :20-34.
- [Google Scholar]
- Application and development trend of artificial intelligence in petroleum exploration and development. Pet. Explor. Dev.. 2021;48(1):1-14.
- [Google Scholar]
- Characterization of tight-gas sand reservoirs from horizontal-well performance data using an inverse neural network. J. Nat. Gas Sci. Eng.. 2018;59:35-46.
- [Google Scholar]
- Optimized application of geology-engineering integration data of unconventional oil and gas reservoirs. China Petrol. Explor.. 2019;24:147-152.
- [Google Scholar]
- Back-to-back competitive learning mechanism for fuzzy logic based supervisory control system of hybrid electric vehicles. IEEE Trans. Ind. Electron.. 2020;67(10):8900-8909.
- [Google Scholar]
- Application status and prospect of big data and artificial intelligence in oil and gas field development. J. China Univ. Petrol. (Nat. Sci. Ed.). 2020;44:1-11.
- [Google Scholar]
- Applications of artificial intelligence in oil and gas development. Arch. Comput. Methods Eng.. 2021;28(3):937-949.
- [CrossRef] [Google Scholar]
- Practices of highly efficient exploration in the Kelameili gas field, Junggar Basin. Nat. Gas Ind.. 2010;30:7-10.
- [Google Scholar]
- Application of geology-engineering integration in the development of tight gas reservoir in Sulige Gasfield. China Petrol. Explor.. 2017;22:53-60.
- [Google Scholar]
- Application and influence of artificial intelligence in petroleum engineering area. Petroleum technology.. 2018;4:32-40.
- [Google Scholar]
- Designing a robust proppant detection and classification workflow using machine learning for subsurface fractured rock samples post hydraulic fracturing operations. Journal of PetroleumScience and Engineering.. 2019;172:588-606.
- [Google Scholar]
- Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation. J. Petrol. Sci. Eng.. 2018;171:1211-1222.
- [Google Scholar]
- Artificial intelligence techniques for sizing photovoltaic systems: a review. Renewable Sustainable Energy Rev.. 2009;13(2):406-419.
- [Google Scholar]
- A methodological approach for reservoir heterogeneity characterization using artificial neural networks. In: Annual Technical Conference and Exhibition, 25–28 September. New Orleans, Louisiana: Society of Petroleum Engineers; 1994. p. :12-21.
- [Google Scholar]
- Computer-aided segmentation of liver lesions in CT scans using cascaded convolutional neural networks and genetically optimised classifier. Arab. J. Sci. Eng.. 2019;44(4):4049-4062.
- [Google Scholar]
- Application of artificial intelligence techniques in drilling system design and operations: a state of the art review and future research pathways. In: Nigeria Annual International Conference and Exhibition, 2–4 August. Lagos, Nigeria: Society of Petroleum Engineers; 2016. p. :23-41.
- [Google Scholar]
- Integration of oilfield planning problems: infrastructure design, development planning and production scheduling. J. Petrol. Sci. Eng.. 2017;158:585-602.
- [Google Scholar]
- New insights into porosity determination using artificial intelligence. Petrolum. 2018;4:408-418.
- [Google Scholar]
- A critical overview of artificial neural network applications in the context of continuous oil field optimization. In: Annual Technical Conference and Exhibition, 29 September-2 October. San Antonio, Texas: Society of Petroleum Engineers; 2002. p. :22-31.
- [Google Scholar]
- Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme. J. Petrol. Sci. Eng.. 2009;64:25-34.
- [Google Scholar]
- Oil and gas well test design and real-time optimization decision-making system. China University of Petroleum (Beijing); 2018. Mater Dissertation Research Paper
- Transient pressure behavior of acid fracturing well in tidal flat carbonate reservoirs. In: Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, 23–26 April. Dammam, Saudi Arabia: Society of Petroleum Engineers; 2018. p. :35-70.
- [Google Scholar]
- Geological modeling of shale gas reservoir during the implementation process of geology-engineering integration. China Petrol. Explor.. 2020;25:83-95.
- [Google Scholar]
- Development characteristics and direction of tight oil and gas in China. Pet. Explor. Dev.. 2019;46:1015-1026.
- [Google Scholar]
- Risk analysis speed-up with surrogate models. In: Latin America and Caribbean Petroleum Engineering Conference, 16–18 April. Mexico City, Mexico: Society of Petroleum Engineers; 2012. p. :22-30.
- [Google Scholar]
- Distribution and potential of global oil and gas resources. Pet. Explor. Dev.. 2018;45(4):779-789.
- [Google Scholar]
- Exploration on geology-engineering integration of hard-to-recover reserves in Shengli Oilfi eld. China Petrol. Explor.. 2020;25:43-50.
- [Google Scholar]
- A hybrid analytic solution for a well with a finite-conductivity vertical fracture. J. Petrol. Sci. Eng. 2020
- [CrossRef] [Google Scholar]
- Application and effect of geology-engineering integration in the exploration and development of Baod CBM field. China Petrol. Explor.. 2018;23:69-75.
- [Google Scholar]
- Fault detection based on fully convolutional networks (fcn) J. Mar. Sci. Eng.. 2021;9(3):259.
- [CrossRef] [Google Scholar]
- Convolutional neural network algorithm for classification evaluation of fractured reservoirs. Petrol. Geophys. Prospect.. 2018;57:618-626.
- [Google Scholar]
- Practice and cognition on zipper fracturing of horizontal well group in Jiaoshiba block. Fuling China Petrol. Explor.. 2018;23:51-58.
- [Google Scholar]
- Practice of geology-engineering integration in Changning State Shale Gas Demonstration Area. China Petrol. Expl.. 2017;22:21-28.
- [Google Scholar]
- “Exploring oil inside source kitchen”: main types of tight oil and progress of geology-engineering integration. China Petrol. Explor.. 2020;25:73-83.
- [Google Scholar]
- “Exploring petroleum inside source kitchen”: connotation and prospects of source rock oil and gas. Pet. Explor. Dev.. 2019;46(1):181-193.
- [Google Scholar]
- The path analysis of strengthening the cost budget control of oil enterprises. Modern Econ.. 2014;11:16-17.
- [Google Scholar]
- Parameter fitting method for reservoir physical properties based on particle swarm optimization and support vector machine. J. Xi'an Polytech. Univ.. 2016;30:699-704.
- [Google Scholar]
- Pre-stack stochastic inversion based on hybrid genetic algorithm. J. China Univ. Petrol. (Nat. Sci. Ed.). 2017;41:65-70.
- [Google Scholar]
- Research on crude oil storage and transportation based on optimization algorithm. In: Advances in materials electronics II: Proceedings of the 2nd International Conference on Advances in Materials, Machinery, Electronics (AMME 2018). 2018.
- [Google Scholar]
- The innovation and practice of integration mode for development of KL3-2 oilfield group. China Offshore Oil Gas. 2013;25:67-70.
- [Google Scholar]
- Zhao, X.Z., Zhao, P.Q., Li D.P., 2018. Research and practice of geology-engineering integration in.
- Theory, technology and prospect of natural gas. Pet. Explor. Dev.. 2018;45:575-587.
- [Google Scholar]
