Investigation of probabilistic models for forecasting the efficiency of proppant hydraulic fracturing technology
To solve the problems accompanying the development of forecasting methods, a probabilistic method of data analysis is proposed. Using a carbonate object as an example, the application of a probabilistic technique for predicting the effectiveness of proppant hydraulic fracturing (HF) technology is considered. Forecast of the increase in the oil production of wells was made using probabilistic analysis of geological and technological data in different periods of HF implementation. With the help of this method, the dimensional indicators were transferred into a single probabilistic space, which allowed performing a comparison and construct individual probabilistic models. An assessment of the influence degree for each indicator on the HF efficiency was carried out. Probabilistic analysis of indicators in different periods of HF implementation allowed identifying universal statistically significant dependencies. These dependencies do not change their parameters and can be used for forecasting in different periods of time. Criteria for the application of HF technology on a carbonate object have been determined. Using individual probabilistic models, integrated indicators were calculated, on the basis of which regression equations were constructed. Equations were used to predict the HF efficiency on forecast samples of wells. For each of the samples, correlation coefficients were calculated. Forecast results correlate well with the actual increase (values of the correlation coefficient r = 0.58-0.67 for the examined samples). Probabilistic method, unlike others, is simple and transparent. With its use and with careful selection of wells for the application of HF technology, the probability of obtaining high efficiency increases significantly.
- Galkin V.I. Kazantsev A.S., Koltyrin A.N. Probabilistic-Statistical Estimation of Different Indicators Used to Determine the Efficiency of a Formation Proppant Hydraulic Fracturing (On the Example of Tl-Bb Terrigenous Formation And V3V4 Carbonate Formation). Oilfield Engineering. 2018. N 2, p. 26-23. DOI: 10.30713/0207-2351-2018-2-26-33 (in Russian).
- Galkin V.I., Ponomareva I.N., Koltyrin A.N. Development of Probabilistic and Statistical Models for Evaluation of the Ef-fectiveness of Proppant Hydraulic Fracturing (On Example of the Tl-Bb Reservoir of the Batyrbayskoe Field). Perm Journal of Pe-troleum and Mining Engineering. 2018. Vol. 18. N 1, p. 37-49. DOI: 10.15593/2224-9923/2018.1.4 (in Russian).
- Galkin V.I., Sosnin N.E. Geological Development of Mathematical Models for the Prediction of Oil and Gas Complex-Built Structures in the Devonian Clastic Sediments. Oil Industry. 2013. N 4, p. 28-31. (in Russian)
- Savchenko P.D., Fedorov A.I., Kolonskikh A.V., Urazbakhtin R.F., Davletova A.R. Method for Selecting Well Candidates Based on the Effect of Fracture Reorientation. Oil Industry. 2017. N 11, p. 114-117. DOI: 10.24887/0028-2448-2017-11-114-117 (in Russian).
- Sosnin N.E. Development of Statistical Models for Predicting Oil-And-Gas Content (On the Example of Terrigenous Devo-nian Sediments of North Tatar Arch). Perm Journal of Petroleum and Mining Engineering. 2012. Vol. 11. N 5, p. 16-25 (in Russian).
- Alimkhanov R., Samoylova I. Application of Data Mining Tools for Analysis and Prediction of Hydraulic Fracturing Efficiency for the BV8 Reservoir of the Povkh Oil Field. SPE Russian Oil and Gas Exploration & Production Technical Conference and Exhibition, 14-16 October 2014. Moscow, Russia, 2014. SPE-171332-RU. DOI: 10.2118/171332-RU (in Russian).
- Anifowose F., Abdulraheem A. Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization. Journal of Natural Gas Science and Engineering. 2011. Vol. 3. N 3, p. 505-517. DOI: 10.1016/j.jngse.2011.05.002
- Aryanto А., Kasmungin S., Fathaddin F. Hydraulic fracturing candidate-well selection using artificial intelligence approach. Journal of Natural Gas Science and Engineering. 2018. Vol. 2. N 2, p. 53-59. DOI: 10.33021/jmem.v2i02.322
- Ashena R., Moghadasi J. Bottom hole pressure estimation using evolved neural networks by real coded ant colony optimization and genetic algorithm. Journal of Petroleum Science and Engineering. 2011. Vol. 77. N 3-4, p. 375-385. DOI: 10.1016/j.petrol.2011.04.015
- Gong X., Gonzalez R., McVay D., Hart J.D. Bayesian Probabilistic Decline Curve Analysis Quantifies Shale Gas Reserves Uncertainty. Canadian Unconventional Resources Conference, 15-17 November 2011, Calgary, Alberta, Canada, 2011. SPE 147588. DOI: 10.2118/147588-MS
- Clark A.J., Lake L.W., Patzek T.W. Production Forecasting with Logistic Growth Models. SPE Annual Technical Confer-ence and Exhibition, 30 October – 2 November 2011, Denver, Colorado, USA, 2011. SPE 144790-MS. DOI: 10.2118/144790-MS
- Yu T., Xie X., Li L., Wu W. Comparison of Candidate-Well Selection Mathematical Models for Hydraulic Fracturing. Fuzzy Systems & Operations Research and Management. Springer, Cham, 2015. Vol. 367. P. 289-299. DOI: 10.1007/978-3-319-19105-8_27
- Ma X., Liu Z. Predicting the oil field production using the novel discrete GM (1, N) model. The Journal of Grey System. 2015. Vol. 27. Iss. 4, p. 63-73.
- Mattar L. Production Analysis and Forecasting of Shale Gas Reservoirs: Case History-Based Approach. SPE Shale Gas Production Conference, 16-18 November 2008. Fort Worth, Texas, USA, 2008. SPE 119897-MS. DOI: 10.2118/119897-MS
- McVay D.A., Dossary M.N. The Value of Assessing Uncertainty. SPE Journal. 2014. Vol. 6. Iss. 2, p. 100-110. DOI: 10.2118/160189-PA
- Mohaghegh S., Reeves S., Hill D. Development of an intelligent systems approach for restimulation candidate selection. SPE/CERI Gas Technology Symposium, 3-5 April 2000, Calgary, Alberta, Canada, 2000. SPE-59767-MS. DOI: 10.2118/59767-MS
- Petrakov D.G., Kupavykh K.S., Kupavykh A.S. The effect of fluid saturation on the elastic-plastic properties of oil reservoir rocks. Curved and Layered Structures. 2020. Vol. 7. N 1, p. 29-34. DOI: 10.1515/cls-2020-0003
- Podoprigora D.G., Saychenko L.A. Development of acid composition for bottom-hole formation zone treatment at high reservoir temperatures. Espacios. 2017. Vol. 48. N 38, p. 32.
- Cheng Y., Wang Y., McVay D., Lee W.J. Practical Application of a Probabilistic Approach to Estimate Reserves Using Production Decline Data. SPE Economics and Management. 2010. Vol. 2. Iss. 1, p. 19-31. DOI: 10.2118/95974-PA
- Rahmanifard H, Plaksina T. Application of artifcial intelligence techniques in the petroleum industry: a review. Artifcial Intelligence Review. 2019. Vol. 52, p. 2295-2318. DOI: 10.1007/s10462-018-9612-8
- Sandyga M.S., Struchkov I.A., Rogachev M.K. Formation damage induced by wax deposition: laboratory investigations and modeling. Journal of Petroleum Exploration and Production Technology. 2020. Vol. 10. N 6, p. 2541-2558. DOI: 10.1007/s13202-020-00924-2
- Schapire R.E., Freund Y. Boosting. Foundations and algorithms. Cambridge: The MIT Press, 2012, p. 544.
- Yanfang W., Salehi S. Refracture candidate selection using hybrid simulation with neural network and data analysis techniques. Journal of Petroleum Science and Engineering. 2014. Vol. 123, p. 138-146. DOI: 10.1016/j.petrol.2014.07.036