Fuzzy logic and grey clustering analysis hybrid intelligence model applied to candidate-well selection for hydraulic fra
- PDF / 2,025,669 Bytes
- 13 Pages / 595.276 x 790.866 pts Page_size
- 58 Downloads / 148 Views
ORIGINAL PAPER
Fuzzy logic and grey clustering analysis hybrid intelligence model applied to candidate-well selection for hydraulic fracturing in hydrocarbon reservoir Bo Gou 1 & Chuan Wang 1 & Ting Yu 2 & Kunjie Wang 3 Received: 18 November 2019 / Accepted: 2 September 2020 / Published online: 17 September 2020 # Saudi Society for Geosciences 2020
Abstract Candidate-well selection (CWS) aims to recognize wells that have potential for higher production after hydraulic fracturing stimulation in petroleum development process, which is natural nonlinear, strong-coupling, uncertain, multi-input, and singleoutput mathematical problem. CWS hybrid intelligence model is developed by integrating widely applied fuzzy logic systems (FLS), namely, type-2 Takagi-Sugeno-Kang (T2-TSK) FLS, with grey clustering analysis (GCA) for hydraulic fracturing in H gas field of Sichuan Basin, one of the large natural gas field in Southwest of China. The T2-TSK FLS is constructed based on field data involving 49 fractured wells, while the GCA is used to determine the dominant input variables data, and these dominant variables have great influence on post-fractured production. Then we use 39 fractured wells data to train the T1-TSK and T2-TSK FLS to predict post-fractured production. The accuracy of the trained models is validated by comparing predicted post-fractured production with real post-fractured production for the rest of the 10 fractured wells. The evaluation results for the gas field case demonstrate that the T2-TSK FLS is superior to the traditional T1-TSK FLS for CWS using the same input data. The T2-TSK FLS developed in this paper gives high accuracy predicted post-production in H gas field, which is very helpful in selecting the candidate well exactly for hydraulic fracturing. Keywords T2-TSK FLS . T1-TSK FLS . Grey clustering analysis . Candidate-well selection . Hydraulic fracturing . Hydrocarbon reservoir
Introduction Hydraulic fracturing (HF) treatment is a complex systematic engineering, including three critical and interactional parts: candidate-well selection (CWS), stimulation treatment design, Responsible Editor: Santanu Banerjee * Bo Gou [email protected]; [email protected] * Ting Yu [email protected] 1
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, Sichuan, China
2
School of Science, Southwest Petroleum University, Chengdu 610500, Sichuan, China
3
Sinopec Southwest Petroleum Engineering co. LTD, Deyang 618000, Sichuan, China
and field application. CWS is the process of identifying wells that have capacity of higher production and better return of investment after stimulation treatment based on reservoir geology and stimulation data. The wrong CWS will result in a failure of HF; therefore, it is considered as the first and a crucial decision-making phase in the entire process of HF stimulation. A good number of studies have been carried out on the CWS investigation. The CWS methods often are divided into two types: conventional and advanced
Data Loading...
