«Onshore and offshore oil and gas well construction»

The technical and economic effect of the introduction of digital technologies to improve the quality of well cementing in the fields of Western Siberia

UDC: 622.245.422
DOI: -

Authors:

¹ LUKOIL-Engineering, Moscow, Russia
² Tyumen Industrial University, Tyumen, Russia

Keywords: quality of cementing, tightness of case, machine learning, the Random Forest, Bayes' theorem

Annotation:

The share of oil and gas revenues in the Russian budget for 2025 is projected at more than 27 %. The efficiency of related industries depends on the volume of production and the quality of the extracted hydrocarbons. To ensure the required level of oil production, it is necessary to drill new wells as the most effective way to develop oil and gas fields. The key stage of well construction is cementing, which determines the tightness of the support, and as a result, the duration of non-stop and trouble-free production of hydrocarbons. At the moment, standard technical and technological solutions to improve the quality of well cementing have reached the technological limit. The reason for this is that the solutions used are narrowly focused – changes mainly relate to the composition or type of grouting solutions or the use of new cementing equipment (modified cementing units, equipment for rotating casing strings, etc.). The authors of the article consider the technical and economic effect of the introduction of technological solutions developed using machine learning algorithms (Decision Forest and Bayes' theorem), which optimize the entire process of well construction. As a result of industrial testing at 64 wells drilled in the fields of Western Siberia, an increase in the proportion of continuous contact, uniformity of grouting solution was achieved, tightness of the support of experimental wells was ensured, and the proportion of unproductive time for well construction was reduced.

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