Improving the selection of object-analogues of oil and gas fields in designing reservoir engineering - Alexander V. Karsakov; Pavel N. Zyatikov; Kirill V. Sinebriukhov; Irina V. Sharf (Известия Томского политехнического университета: Инжиниринг георесурсов)
Relevance. The commissioning of a large number of new fields with limited amount of initial geological and physical data. To fill in the missing data, the selection of object-analogs is carried out. From the correct choice the selected development system will depend. But according to the established practice, the choice of analogues is carried out only by the expert method, based on the search for the geographically closest objects being developed. The effectiveness of the chosen development strategy depends on the selected analogues, which in their turn will minimize the risks of oil companies during the operation of assets. Aim. Development of an algorithm for qualitatively selection of the best object-analogue of the project field, taking into account the verification of the selected analogues. Methods. Evaluation and analysis of the necessary data to define the degree of similarity of reservoir development by the methods of mathematical statistics and machine learning. Results. The authors describe the problem in the selection of objecvt-analogues and the existing approaches to its solution. The paper introduces the prospects and possibilities of applying the accumulated experience in the developing of new assets and provides an algorithm for the selection of analogues based on a qualitative assessment of geological parameters and quantitative assessment of the degree of similarity of geological and physical characteristics of the object. The results obtained show that the method allows you to quickly find analogues from massive databases, and has a high degree of correlation with the variants of deposits agreed upon by the state expertise for the development of hydrocarbon fields. The authors proposed a way of applying the analogy method to predict the missing data.