Prediction of fluid brine event zones by artificial intelligence methods based on new generation RTH seismic attributes and drilling data at the Kovykta gas condensate field

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Аннотация

A new method for predicting lithofacies, gas fluid and brine zones, zones with abnormally high reservoir pressure, as well as petrophysical properties of rocks using artificial intelligence methods based on a family of new seismic attributes of the RTH method and well drilling data is proposed. The main difference between RTH attributes and traditional ones obtained by migration transformation is their voxel nature and hyperattributive. It turned out that this is a key advantage of the new approach to solving problems of geological forecasting using artificial intelligence methods. The paper presents the results of applying a new method for processing and interpreting modern 3D seismic data, as well as geological forecasting based on it for the area of intense brine occurrence of the Kovykta gas condensate field.

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Авторлар туралы

A. Bugaev

V.A. Kotelnikov Institute of Radio Engineering and Electronics, Russian Academy of Sciences

Email: Gerokhin@kantiana.ru

Academician of the RAS

Ресей, Moscow

G. Erokhin

I. Kant Baltic Federal University

Хат алмасуға жауапты Автор.
Email: Gerokhin@kantiana.ru
Ресей, Kaliningrad

S. Ryabykh

“GIRS-M” LLC

Email: Gerokhin@kantiana.ru
Ресей, Moscow

A. Smirnov

Gazprom VNIIGAZ LLC

Email: Gerokhin@kantiana.ru
Ресей, Tyumen

Әдебиет тізімі

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2. Fig. 1. The research area of the KGKM of the area of intensive rapoprevention with AVPD.

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3. Fig. 2. Comparison of the results of temporary migration of PSTM (a) and RTH ATD (b).

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4. Fig. 3. A map of the average values of fracturing by RTH in the interval of the Atov horizon KGKM in cu.

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5. Fig. 4. A map of the average values of the angle of deviations from the vertical of the maximum scattering in the interval of the Atom horizon KGKM in degrees (positive values of the angle – clockwise).

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6. Fig. 5. Vertical cross section (a) and a map of the average values in the interval of the Bilchir horizon (b) of the cube of RTH velocity in m/s. The voxel size is 50x50x10 meters.

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7. Fig. 6. Comparison of the forecast of the rapid occurrence in the range of the Bilchir horizon of the KGKM by the scattered wave method [19] and the MLP method based on RTH attributes and drilling data (b). Red is the maximum probability (0.4), blue is the minimum probability.

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8. Fig. 7. Forecast by the RF method based on RTH attributes and drilling data for gas occurrences in the Khristoforovo-Balykhtinsky horizon of the KGKM. Red is the maximum probability (0.25), blue is the minimum probability. The average probability value is 0.018, the deviation is 0.23.

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9. Fig. 8. Forecast by the RF method based on RTH attributes and drilling data in the supra-Parfenovian range of fluid manifestation formations (a). Red is the maximum probability (0.7), blue is the minimum probability. The average probability value is 0.059, the deviation is 0.64. The profile diagram on the KGKM site (b).

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10. Fig. 9. Forecast of average porosity by the RF method in the roof–sole interval of the lower part of the Parthenovsky ridge according to RTH attributes and drilling data. The color scale ranges from 20% porosity (red) to 12% (blue).

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11. Fig. 10. Comparison of the average porosity in the lower part of the Parthenovian horizon, predicted by the RF method based on RTH attributes (red) and constructed according to GIS data after spatial averaging (green) for a number of wells. The vertical scale is the voxel number along the borehole. Voxel dimensions are 25x25x5 m. The well highlighted in blue did not participate in the training.

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