Development of a comprehensive methodology for the forecast of effectiveness of geological and technical measures based on machine learning algorithms

Abstract

The main part of hydrocarbon production in Russia is represented by old oil and gas producing regions. Such areas are characterized by a significant decrease in well productivity due to high water cut and faster production of the most productive facilities. An important role for such deposits is played by stabilization of production and increase of mobile reserves by improving the development system. This is facilitated by various geological and technical measures. Today, an urgent problem is to increase the reliability of the forecast of technological and economic efficiency when planning various geological and technical measures. This is due to the difficulty in selecting candidate wells under the conditions of the old stock, the large volume of planned activities, the reduction in the profitability of measures, the lack of a comprehensive methodology for assessing the potential of wells for the short and long term. Currently, there are several methods to evaluate the effectiveness of geological and technical measures: forecast based on geological and field analysis, statistical forecast, machine learning, hydrodynamic modeling. However, each of them has its own shortcomings and assumptions. The authors propose a methodology for predicting the effectiveness of geological and technical measures, which allows one to combine the main methods at different stages of evaluating the effectiveness and to predict the increase in fluid and oil production rates, additional production, changes in the dynamics of reservoir pressure and the rate of watering of well production.

Prospects for the use of advanced computer modeling systems in planning geological and technical activities

“…According to the research results, more than half of the oil and oilfield service companies in the Russian Federation do not attach such high importance to the above attributes compared to their foreign counterparts [9]. The availability of an extensive amount of historical information about the object allows not only to avoid uncertainties when planning a portfolio of geotechnical measures in order to intensify the processes occurring in the productive reservoir, but also contributes to making the right decisions when choosing techniques and technologies to limit the amount of associated produced water.…”

“…Repair and insulation works are a set of technological operations to prevent the entry of water, sand or a mixture into a corrosive object (well) and its operation zone (annular space), disconnecting layers, intervals, and the build-up of a cement ring behind the casing from it for various reasons. This type of geological and technical measures, together with additional well operations, is one of the methods of implementing work to increase the oil recovery coefficient, protect the subsoil and the natural environment [9][10][11].…”

Possibility of using machine learning algorithms to assess the effectiveness of acid hydraulic fracturing

“…The model allows us to accurately predict the potential increase in oil production (R=0.73). At the next stage, various machine-learning methods are used to select the optimal method for predicting oil production based on a set of geological and technological parameters, which can be effective in developing a comprehensive methodology for predicting the effectiveness of geological and technical measures [10].…”

Composite model of seismic monitoring data analysis during mining operations on the example of the Kukisvumchorrskoye deposit of AO Apatit

“…As a result of the work, the HDBSCAN algorithm was applied [6, 25,26], which is a hierarchical spatial algorithm for clustering data with noise based on the use of distribution density [27][28][29][30].…”