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.

This study presents a methodological approach to forecasting the efficiency of radial drilling technology under various geological and physical conditions. The approach is based upon the integration of mathematical statistical methods and building machine learning models to forecast the liquid production rate increment, as well as to forecast technological indexes using a hydrodynamic model. This paper reviewed the global practice of radial drilling and well intervention efficiency modeling. The efficiency of the technology in question was analyzed on the oil deposits of the Perm Territory. Mathematical statistical methods were used to determine the geological and technological parameters of the efficient technology use. Based on the determined parameters, machine learning models were built, allowing us to forecast the oil and liquid production rate. A script was developed to integrate machine learning methods into a hydrodynamic simulator. When the method was tested, the deviations in the difference between the actual and the forecast cumulative oil production did not exceed 10%, which proves the reliability of the method. At the same time, the hydrodynamic model allows for taking into account the mutual influence of oil wells, the dynamics of water cut, and reservoir pressure.

Amid the ever-increasing urgency to develop oil fields with complex mining and geological conditions and low-efficiency reservoirs, in the process of structurally complex reservoir exploitation a number of problems arise, which are associated with the impact of layer fractures on filtration processes, significant heterogeneity of the structure, variability of stress-strain states of the rock mass, etc. Hence an important task in production engineering of such fields is a comprehensive accounting of their complex geology. In order to solve such problems, the authors suggest a methodological approach, which provides for a more reliable forecast of changes in reservoir pressure when constructing a geological and hydrodynamic model of a multi-layer field. Another relevant issue in the forecasting of performance parameters is accounting of rock compressibility and its impact on absolute permeability, which is the main factor defining the law of fluid filtration in the productive layer. The paper contains analysis of complex geology of a multi-layer formation at the Alpha field, results of compression test for 178 standard core samples, obtained dependencies between compressibility factor and porosity of each layer. By means of multiple regression, dependencies between permeability and a range of parameters (porosity, density, calcite and dolomite content, compressibility) were obtained, which allowed to take into account the impact of secondary processes on the formation of absolute permeability. At the final stage, efficiency of the proposed methodological approach for construction of a geological and hydrodynamic model of an oil field was assessed. An enhancement in the quality of well-by-well adaptation of main performance parameters, as well as an improvement in predictive ability of the adjusted model, was identified.

At any stage of field development, the process of developing and history-matching geologic and hydrodynamic models has many uncertainties. To improve reliability of geologic and hydrodynamic models all the available information shall be used. For undeveloped fields, these are the results of hydrodynamic well tests at the stage of early production, and for fields with a high degree of exploration all the available information shall be objectively integrated. This paper considers various approaches to improvement of geological and hydrodynamic models’ reliability. The authors propose a method of hydrodynamic model history-matching to indicator diagrams results. To refine permeability cube at underexplored fields, it is proposed to history-match the geologic and hydrodynamic model to the results of hydrodynamic tests carried out at exploration stage. For fields with a high degree of exploration, it is proposed to integrate different studies. Linear discriminant analysis was used for this purpose. As a result, this allowed to significantly reduce the model adaptation time and increase its predictive reliability.