Efficient development of oil rims requires tools for making production forecasts to consider not only in situ processes, but also limitations of surface infrastructure. This paper describes a case study of creating an integrated structural model of field X containing a block with 30 wells confined to an area of about 20 million m2. Oil accumulations in this field are represented by marginal and underlying oil rims. The basic concept of oil rim development in the field is re-injection of the produced associated gas back into the reservoir. Volumes of gas injected to maintain reservoir pressure affect oil production levels. Conversely, efficient oil well operation depends on associated gas production, including the gas breaking through from gas caps. With this development strategy, it is critical to consider the limitations of artificial lift systems, production gathering, and treatment systems to maximize economic performance of the project and to generate reliable oil, gas, and water production forecasts. Integrated modeling is aimed at a detailed approach of predicting production levels by combining the reservoir, well, and surface gathering systems into a single generalized model. Such model should account for processes taking place in both the reservoir and surface gathering systems, as well as limitations such as gas and fluid velocities in well lifting and gathering network systems, design limitations of gas and oil treatment facilities, and pipeline loops and jumpers, with the final objective of improving the quality of decision-making. The paper describes the basic steps of building an integrated model and the results of comparing production forecasts based on a conventional hydrodynamic model with those based on an integrated model. Calculations proved that well interventions are required for a more efficient field development to optimize production rates from the producing wells.
In arid conditions of the Steppe zone of Ukraine for obtaining stable yields of lucerne and observance the conditions of resource-saving, it is important to know from what factors the value of the yield of lucerne depends on. According to the results of the conducted research, an agroecological model of the productivity of growing crop on irrigated lands of the Ukrainian Steppe has been formed. For the carrying out research, the method of artificial neuron networks was used. Creating an agroecological model of lucerne production using neuron networks consists of the following phases: search of data; preparation and normalization of data; choice of type of neuron network; experimental choice of network characteristics; experimental choice of parameters; obtaining an artificial neuron network for modeling the productivity of lucerne; checking of adequacy of the model; adjustment of parameters, final training. As a result of the research it was found that artificial neuron networks are fundamentally different from traditional methods of statistical data analysis. In the capacity of main elements of the system are taken: the sum of effective temperatures above +5 °С; amount of atmospheric precipitation; solar lighting duration; irrigation norms; depth of soil tillage; fertilization and plant protection. The article presents a constructed neuron network with architectural parameters. It has been established that among the significant number of natural and agrotechnical factors affecting the productivity of crops of lucerne, the greatest influence is carried out by atmospheric precipitation and, in our case, water-saving irrigation norms. Among the investigated factors there are a high degree of pair and multiple correlations. It is proved that the components of architecture contain different compositions of multilayered perceptrons, radial-basic functions, and also linear components.
The work is devoted to highlighting the experience of creating a full-scale integrated (reservoir – well – infrastructure network) model of gas reservoirs of the large oil, gas and condensate field of Yamal, finding optimal solutions to the problem of both development oil rims, gas caps of productive reservoirs and dry gas reservoirs, as well as the gas injection system on the efficiency of the field development process as a whole. The task included creating, history matching and integration of the hydrodynamic models with surface facility system, the elimination of "bottlenecks" in the gas facility network system and the determination of the optimal solution to the problem of hydrocarbon production (oil production from oil rims and gas production from gas caps and dry gas formations). Conducted integrated calculations allowed us to consistently identify the risks of the project and allowed them to be minimized at the preparatory stage of the project, which leads to an increase in the value of the project.
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