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The paper presents a complex approach based on the experience of the authors of this article for creating and maintaining integrated asset models (IAM) and implementing a digital data management platform. Problems of using IAM for the operational management of field development and production are that the data is not accurate, the measurements are spaced in time, and there is not enough data to understand the physical phenomena taking place. The complex approach is that to provide integrated asset models with high-quality data, it is necessary to build new processes, create new specialties and competencies, the key success factor is the combination of the experience of Customer (oil company), Internal oil-related service of Customer (geological and geophysical research), External contractor of oil-related service (the combination of experience in geological and geophysical research, experience in integrated asset modeling and operational support for field development using integrated asset modeling tools and digitalization of data management). The best way to implement the approach of creating joint Integrated Team of External and Internal oilfield service Contractors in the form of Complex Service Engineering Center, the task for which which was the organization of a cyber-physical system for collecting field data, verifying data, identifying problem areas in data, defining approaches to eliminating problem areas using tools of automation tools for working with data, the flexible management of well testing and survey programs, the operational formation of well testing and survey design for non-standard situations. Particular attention in this complex approach is paid to working with initial field data, this article provides a general scheme for verifying the various parameters of well operation and an example of its use for flow rates, as well as examples of the quality analysis of reservoir pressures based on the use of a two-dimensional one-phase proxy reservoir model and the quality analysis of GOR for a well. Based on the developed complex approach, the paper provides examples of strategic and operational problems for a field - the assessment of optimal production for a field and the assessment of oil shortfalls for a well, respectively.
The paper presents a complex approach based on the experience of the authors of this article for creating and maintaining integrated asset models (IAM) and implementing a digital data management platform. Problems of using IAM for the operational management of field development and production are that the data is not accurate, the measurements are spaced in time, and there is not enough data to understand the physical phenomena taking place. The complex approach is that to provide integrated asset models with high-quality data, it is necessary to build new processes, create new specialties and competencies, the key success factor is the combination of the experience of Customer (oil company), Internal oil-related service of Customer (geological and geophysical research), External contractor of oil-related service (the combination of experience in geological and geophysical research, experience in integrated asset modeling and operational support for field development using integrated asset modeling tools and digitalization of data management). The best way to implement the approach of creating joint Integrated Team of External and Internal oilfield service Contractors in the form of Complex Service Engineering Center, the task for which which was the organization of a cyber-physical system for collecting field data, verifying data, identifying problem areas in data, defining approaches to eliminating problem areas using tools of automation tools for working with data, the flexible management of well testing and survey programs, the operational formation of well testing and survey design for non-standard situations. Particular attention in this complex approach is paid to working with initial field data, this article provides a general scheme for verifying the various parameters of well operation and an example of its use for flow rates, as well as examples of the quality analysis of reservoir pressures based on the use of a two-dimensional one-phase proxy reservoir model and the quality analysis of GOR for a well. Based on the developed complex approach, the paper provides examples of strategic and operational problems for a field - the assessment of optimal production for a field and the assessment of oil shortfalls for a well, respectively.
Summary The article reviews the development and implementation of a digital twin for one of the large fields of LUKOIL-West Siberia LLC. The project team has developed an integrated asset model (IAM) of an oil field at a late stage of its development, which is used both for making managerial decisions and in the operational work of the engineering and technical service. The IAM includes simplified models of reservoirs, models of wells and gathering systems, as well as simplified models of plants. The resulting model can produce short-term assumptions regarding production levels (up to 1 year) and is highly sustainable, which is confirmed by the examples given in this article as to the application of IAM for various production tasks. The developed automated tools allow making prompt decisions to optimize well stock operation, as well as to reveal deviations in the process parameters of downhole pumping equipment and metering facilities. The use of IAM tools enable production functions to perform many application tasks related to forecasting well operation modes and evaluating the existing production capacities of the field. The cases presented in this paper serve as a good practice for application of the IM by assets in their activities and can be implemented for similar brownfields.
The paper presents the developed methodology for building simplified reservoir models for integrated asset models (IAM) of oil and gas fields: allocation and substantiation of areas, substantiation of model parameters, substantiation of actual weighted average reservoir pressure for areas, history matching and validation, evaluation of effective injection factors, integration in an IAM, prediction calculations, model updating. The novelty of the methodology is the developed approaches and methods of considering different features of fields with a high extent of automation for areas and fields as a whole. Models based on the material balance method and two-dimensional proxy models of one-phase flow in porous media are used as simplified reservoir models in the paper. The developed methodology has been successfully tested for four oil and gas fields of Russia, which have different geological and production features: a large field with a long development history and a large number of active wells, a field with low permeability in all pay zones and high scopes of new wells commissioning, a field with a gas cap and high gas/oil ratios (GOR) for individual wells, a field with a complex system of reservoirs and tectonic faults and a large number of multi-pay production wells. For three out of four fields, at the moment, the IAMs have been transferred to commercial operation based on the pilot projects performed and are used by field specialists to solve the following problems: quality analysis of reservoir pressure measurements; assessments of actual reservoir pressure trends by areas; assessments of ineffective injection for areas; prediction of reservoir pressure, water cut and GOR profiles for wells (up to one year) for various prediction scenarios, including optimization scenarios (taking into account the limitations of the material balance method).
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