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.
The article describes the original technique of predicting the effectiveness of hydrochloric acid treatment of the bottom hole zone of a carbonate reservoir. The technique consists in determining the technological effectiveness of the oil recovery stimulation procedure at productive wells using hydrodynamic simulation based on the calculated value of skin factor change. In the course of the study, a number of parameters affecting the intensity of flow coefficient decline in the near-wellbore zone during acid treatment have been established. The paper presents a comparison of data on the actual change of the skin factor after acidizing jobs at the fields of Perm Krai (Russian Federation) and the calculated values obtained using the presented technique. This scientific research includes an example of practical application of the proposed technique for the target well of the Kokuyskoye oil field in Perm Krai, its results with a minor deviation coinciding with the actual values of the flow rate. In the conclusion to the study, it is noted that using the proposed technique, recommendations can be provided on the selection of acid composition and technology of its injection into formation for the preliminary evaluation of the cost effectiveness of the designed procedure.
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.
This paper aims to study the applicability of machine-learning algorithms, specifically neural networks, for forecasting the effectiveness of Improved recovery methods. Radial jet drilling is the case operation in this study. Understanding changes in reservoir flow properties and their effect on liquid flow rate is essential to evaluate the radial jet drilling effectiveness. Therefore, liquid flow rate after radial jet drilling is the target variable, while geological and process parameters have been taken as features. The effect of various network parameters on learning quality has been assessed. As a result, conclusions on the applicability of neural networks to evaluate the radial jet drilling potential of wells in various geological conditions of carbonate reservoirs have been made.
Relevance of the research is due to the low proportion of successful hydrochloric acid treatments of near-bottomhole zones of carbonate reservoirs in the Perm region caused by insufficiently careful design and implementation of measures to stimulate oil production. Within the framework of this article, the development of a program is presented, which is based on an algorithm that allows determining the volume and rate of injection for an acid composition into a productive formation corresponding to the maximum economic efficiency during hydrochloric acid treatment. Essence of the proposed algorithm is to find the greatest profit from measures to increase oil recovery, depending on the cost of its implementation and income from additionally produced oil. Operation of the algorithm is carried out on the principle of enumerating the values of the volume and rate of injection for the acid composition and their fixation when the maximum difference between income and costs, corresponding to the given technological parameters of injection, is reached. The methodology is based on Dupuis's investigations on the filtration of fluids in the formation and the results of the experiments by Duckord and Lenormand on the study of changes in the additional filtration resistance in the near-well zone of the formation when it is treated with an acid composition. When analyzing and including these investigations into the algorithm, it is noted that the developed technique takes into account a large number of factors, including the lithological and mineralogical composition of rocks, technological parameters of the injection of a working agent and its properties, well design, filtration properties of the formation, properties of well products. The article provides an algorithm that can be implemented without difficulty using any programming language, for example, Pascal. Selection of the optimal values for the volume and rate of injection is presented in this paper, using the example of a production well at the Chaikinskoye oil field, located within the Perm region. Introduction of the developed algorithm into the practice of petroleum engineering will allow competent and effective approach to the design of hydrochloric acid treatments in carbonate reservoirs without a significant investment of time and additional funds.
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