Benchmarking of techniques for improvement of geological model predictive ability (PK1-3 formation, Vostochno-Messoyakhskoye field)

UDK: 553.98.041

Key words: geological modeling, forecast, continental environment, development drilling

Authors: E.V. Zagrebelnyi (Messoyachanefntegas JSC, RF, Tyumen), B.V. Belozerov, A.S. Bochkov, D.O. Mishina, D.A. Reshetnikov, I.V. Kovalenko (Gazpromneft NTC LLC, RF, Saint-Petersburg)

The Messoyakha project is one of the priority plans of Gazprom Neft Company, exploration of which provides significant incremental oil production. The main object of development is PK_1-3 formation (Pokur suite) which contains considerable oil and gas reserves. PK_1-3 formation, deposited in continental sedimentation environment, is characterized by wide variety of facies with different sand proportion content. It provides high facies differentiation and high uncertainty of extension and position in space of formation sand bodies. At this moment, the field is at the development drilling stage, but new data shows that current geological model has low level of prediction ability. Considering this, the selection and application of such modeling parameters that would allow taking into account reservoir heterogeneity and raise the predictive ability becomes a critical issue of the project.

This study includes analysis of different variations of geological modeling methods and parameters with factoring in their uncertainty, and estimation of the model predictive probability. Furthermore, it contains analysis of all data, which could raise the predictive ability. Multirealisation modeling is performed using most suitable modeling methods, identified according to the results of the complex analysis of initial data and variations of modeling methods. The proportion of sand penetration and the evaluation of its deviation from the fact number serve as parameters for the estimation of predictive ability. Based on the modeling results, a comparison of methods and their predictive ability is performed using different parameters.

Moreover, this study suggests the optimal drilling method for fluvial deposition under the low prediction condition.

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