On the prediction of geochemical parameters (TOC, S1 and S2) by considering well log parameters using ANFIS and LSSVM strategies

被引：17
作者：
Ahangari D. [1 ,2 ]
Daneshfar R. [1 ]
Zakeri M. [2 ]
Ashoori S. [1 ]
Soulgani B.S. [1 ]
机构：
[1] Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz
[2] Department of Geology, Faculty of Earth Sciences, S. Chamran University of Ahwaz
来源：
Petroleum | 2022年 / 8卷 / 02期
关键词：
Adaptive-neuro fuzzy inference system; Least squares support vector machine; Particle swarm optimization; Total organic carbon; Well log parameters;
D O I：
10.1016/j.petlm.2021.04.007
中图分类号：
学科分类号：
摘要：
Geochemical parameters are useful properties to enhance hydrocarbon exploration certainty. Though, attaining these parameters, for instance total organic carbon (TOC), volatile and residual hydrocarbon (S1 & S2) is a challenge for geologists due to the high cost and time consumption. Therefore, addressing this issue has become an interesting subject for many researchers. As a result, on the ground of conventional well logs, vast kinds of methods, for example, back propagation artificial neural network (BPANN), have been introduced to solve this problem. Implementing these kinds of methods brings scientists tremendous amounts of information related to the richness of organic matter in a meantime. However, the precision of the aforementioned method is inadequate and BPANN is affected negatively by local optimum. Therefore, current study cope with this issue and alleviate the uncertainty, Least Squares Support Vector Machine (LSSVM) and Adaptive-Neuro Fuzzy Inference System (ANFIS) algorithms cooperating with the particle swarm optimization (PSO) were suggested as a suitable method to increase the precision of estimating geochemical factors. The data bank for this research was attained from available sources of Shahejie formation from Bohai bay basin located in China, which consists of geochemical and well logging information. Outputs of this study illustrated that ANFIS-PSO and LSSVM-PSO have a great ability to estimate geochemical parameters. The values of R2 obtained for these two models in order to predict the output parameters of TOC, S1 and S2 are equal to 0.6846 & 0.785, 0.6864 & 0.778, and 0.7343 & 0.8128, respectively. The statistical comparison between these models shows that LSSVM-PSO shows a better performance compared to another model. Also, a new attempt was implemented to evaluate the impacts of input parameters on the outputs and the results of sensitivity analysis suggest that transit interval time had the greatest effect on the output parameters. © 2021 Southwest Petroleum University
引用
收藏
页码：174 / 184
页数：10
相关论文
共 70 条
[1]  
Zou C., Et al., Geological characteristics and resource potential of shale gas in China. Petroleum exploration and development, 37, 6, pp. 641-653, (2010)
[2]  
Lin S., Et al., Geochemical characteristics of the source rocks in mesozoic yanchang formation, central ordos basin, J. Earth Sci., 24, 5, pp. 804-814, (2013)
[3]  
Schmoker J.W., Determination of organic content of Appalachian Devonian shales from formation-density logs: geologic notes, AAPG (Am. Assoc. Pet. Geol.) Bull., 63, 9, pp. 1504-1509, (1979)
[4]  
Schmoker J.W., Organic content of devonian shale in western appalachian basin, AAPG (Am. Assoc. Pet. Geol.) Bull., 64, 12, pp. 2156-2165, (1980)
[5]  
Meyer B., Nederlof M., Identification of source rocks on wireline logs by density/resistivity and sonic transit time/resistivity crossplots, AAPG (Am. Assoc. Pet. Geol.) Bull., 68, pp. 121-129, (1984)
[6]  
Passey Q., Et al., A practical model for organic richness from porosity and resistivity logs. AAPG bulletin, 74, 12, pp. 1777-1794, (1990)
[7]  
Amiri Bakhtiar H., Et al., Estimating total organic carbon content and source rock evaluation, applying ΔlogR and neural network methods: ahwaz and Marun oilfields, SW of Iran. Petroleum Science and Technology, 29, 16, pp. 1691-1704, (2011)
[8]  
Khoshnoodkia M., Et al., TOC determination of Gadvan Formation in South Pars Gas field, using artificial intelligent systems and geochemical data. Journal of Petroleum Science and Engineering, 78, 1, pp. 119-130, (2011)
[9]  
Sfidari E., Kadkhodaie-Ilkhchi A., Najjari S., Comparison of intelligent and statistical clustering approaches to predicting total organic carbon using intelligent systems. Journal of Petroleum Science and Engineering, 86, pp. 190-205, (2012)
[10]  
Wang P., Et al., Revised models for determining TOC in shale play: example from Devonian Duvernay shale, Western Canada sedimentary basin, Mar. Petrol. Geol., 70, pp. 304-319, (2016)
1234567