A.V. Pinchuk, E.A. Pylev, E.E. Polyakov, M.A. Tvorogov, I.V. Churikovа
Optimisation of cluster drilling based on integrated seismic attributes and well log data analysis using neural network algorithms: Chayandinsky oil and gas condensate field
DOI 10.31087/0016-7894-2022-2-17-30
Key words: Chayandinsky oil and gas condensate field; geological model; Khamakinsky and Talakhsky horizons; well; prediction of lithotype occurrence; lithology; reservoir; neural network; classification; attribute analysis; seismic attribute; lithology cube.
For citation: Pinchuk A.V., Pylev E.A., Polyakov E.E., Tvorogov M.A., Churikova I.V. Optimisation of cluster drilling based on integrated seismic attributes and well log data analysis using neural network algorithms: Chayandinsky oil and gas condensate field. Geologiya nefti i gaza. 2022;(2):17–30. DOI: 10.31087/0016-7894-2022-2-17-30. In Russ.
Chayandinsky oil, gas and condensate field is one of largest in Russia. The main gas accumulations are found in the Vendian Botuobinsky, Khamakinsky, and Talakhsky pay horizons. The field is confined to the large non-structural trap in the north-eastern part of the Nepsky Arch; the field has a rather complicated geological structure that causes numerous challenges in its development. With the purpose to optimise cluster drilling and improve the efficiency of the Chayandinsky oil, gas and condensate field development, prediction of reservoir occurrence was carried out, and their lithological membership within the yet undrilled development well clusters was updated with adjustments based on the wells drilled. The authors discuss the methodology for integration of lithotypes identified from well log data with seismic data, which is based on application of an innovative neural network algorithm. They present the new method of building the predicted local geological models, which is created by them, including the following: re-interpretation of well log data from development wells; integrated interpretation of seismic and drilling data using the method of trainable neural networks; creating adaptive geological cluster models of pay horizons in the Chayandinsky oil, gas and condensate field. The results were lithology cubes accounting for wells data and probability cubes for identified lithological varieties. Comparison of the obtained lithology cubes with geological modelling results being a part of reserves assessment is presented. The authors note a more differentiated distribution of lithological varieties across the section of pay horizons and, as a consequence, more differentiated maps of net thicknesses. The use of the proposed tool will make it possible to update the distribution of zones with better reservoir properties with the purpose of optimizing the placement of production well clusters and increasing the development efficiency of Vendian terrigenous deposits in the Chayandinsky oil, gas and condensate field.
Anatolii V. Pinchuk
Chief Specialist
Gazprom VNIIGAZ,
15, str. 1, Proyektiruyemy proyezd № 5537, Razvilka, Vidnoe,
Moscow region, 142717, Russia
e-mail: A_Pinchuk@vniigaz.gazprom.ru
Evgenii A. Pylev
Candidate of Geographic Sciences,
Acting Deputy General Director for Science,
Head of the Mineral Reserve Base Development Center
Gazprom VNIIGAZ,
15, str. 1, Proyektiruyemy proyezd № 5537, Razvilka, Vidnoe,
Moscow region, 142717, Russia
e-mail: E_Pylev@vniigaz.gazprom.ru
Evgenii E. Polyakov
Doctor of Geologo-Mineralogical Sciences,
Chief Researcher
Gazprom VNIIGAZ,
15, str. 1, Proyektiruyemy proyezd № 5537, Razvilka, Vidnoe,
Moscow region, 142717, Russia
e-mail: E_Polyakov@vniigaz.gazprom.ru
Mikhail A. Tvorogov ORCiD
Chief Specialist
Gazprom VNIIGAZ,
15, str. 1, Proyektiruyemy proyezd № 5537, Razvilka, Vidnoe,
Moscow region, 142717, Russia
e-mail: M_Tvorogov@vniigaz.gazprom.ru
Irina V. Churikova
Head of Laboratory
Gazprom VNIIGAZ,
15, str. 1, Proyektiruyemy proyezd № 5537, Razvilka, Vidnoe,
Moscow region, 142717, Russia
e-mail: I_Churikova@vniigaz.gazprom.ru
1. Polyakov E.E., Ivchenko O.V., Semenova K.M., Strekozin V.V., Nikul’nikova N.A., Romashchenko S.Yu., Churikova I.V., Kondrat’eva L.A., Kolotushchenko L.D., Trukhin V.Yu., Votyakova T.A. Scientific tasks solved at calculating hydrocarbon reserves of Chayanda oil-gascondensate field. Vesti gazovoi nauki. 2017;31(3):172–186. In Russ.
2. Polyakov E.E., Churikova I.V., Pylev E.A., Churikov Yu.M., Semenov E.O., Simonov A.V. Issues on the Permeability Coefficient Determination by Geophysical Well Logging for the Composite Reservoirs of Vendian Period in the Chayandinskoe Oil and Gas Condensate Field at the Developmen Drilling Stage. Oil and Gas Territory. 2018;(10):30–61. In Russ.
3. Polyakov E.E., Pylev E.A., Churikova I.V., Semenov E.O., Churikov Yu.M. et al. Productivity of complex terrigenous reservoirs of the vendian of the Chayandinskoe field depending on the lithological and petrophysical properties and geological and technical conditions of the opencut of sediments. Oil and Gas Territory. 2017;(12):22–32. In Russ.
4. Pylev E.A., Krylov D.N., Churikov Yu.M., Smirnov A.S., Kozhevnikov S.V., Chupova I.M. State-of-art achievements and issues of field geology and geophysics in the Gazprom PJSC. Vesti gazovoi nauki. 2018;35(3):168–180. In Russ.
5. Churikov Yu.M., Pylev E.A., Silaeva E.A., Churikova I.V. Lithofacies zoning as a basis for updating the dependencies of reservoir properties for complex terrigenous reservoirs of the vendian of the Chayandinskoe oil and gas condensate field. Oil and Gas Territory. 2019;(1):20–41. In Russ.
6. Churikov Yu.M. Regularities of Changes in the Cutoff Values of Formation Reservoir Properties of Productive Reservoirs of Vendian Deposits of the “Power Of Siberia” Gas Transmission System, Depending on the Depth and Facies of Sediments. Oil and Gas Territory. 2019;(6):20–41. In Russ.
7. Churikov Yu.M., Pylev E.A., Churikova I.V., Silaeva E.A. Well-log interpretation models designed for Botuoba horizon of Chayanda oil-gascondensate field using lithofacies zoning of Vendian sediments. Vesti gazovoi nauki. 2019;41(4):142–152. In Russ.
8. Churikov Yu.M., Pylev E.A., Polyakov E.E. Generalized dependencies between reservoir and physical properties of Vendian deposits at fields associated with the “Power of Siberia” gas transportation system. Vesti gazovoi nauki. 2019;41(4):106–120. In Russ.
9. Churikova I.V., Pylev E.A., Semenov E.O., Churikov Yu.M., Semenova E.V., Chudina A.A., Simonov A.V. Distribution and properties of saline Vendian reservoirs belonging to Chayanda oil-gas-condensate field. Vesti gazovoi nauki. 2019;41(4):153–163. In Russ.
10. Hami-Eddine K., Klein P., Richard L., De Ribet B., Grout M. A new technique for lithology and fluid content prediction from prestack data: An application to a carbonate reservoir. Interpretation. 2015;3(1):SC19–SC32. DOI:10.1190/INT-2014-0049.1.
11. Klarner S., Malinovskaya O. Benchmarking probabilistic lithotype prediction from seismic data against neural network-derived results. Explorer. 2020. – Available at: https://www.pdgm.com/resource-library/articles-and-papers/2020/Benchmarking-Probabilistic-Lithotype-Prediction-fr (accessed on 12.07.2021).
12. Klarner S., Kirnos D., Ivanova N., Gritsenko A., Malinovskaya O. Comparing Bayesian and neural network supported lithotype prediction from seismic data. First Break. 2020;38:75–79. DOI:10.3997/1365-2397.fb2020053.