No abstract

Currently, oil and gas companies around the world are paying close attention to the intellectualization of assets. This process is resource intensive, requiring increased requirements for the selection of implementation sites depending on the tasks assigned. In physical terms, the greatest effect of the integrated approach is realized when saving capital investments at the stage of choosing the strategy for developing new large fields. Considering that most of the existing fields are in the late stages of operation against the backdrop of high descending volatility in the cost of oil, the relevance of the positive results of the introduction of the concept of "Intelligent Field" on the underlying assets is increasing. In this paper, the results of the intellectualization of a mature asset (a field in development since the late 1960s) are given as features: the depth of the oil-bearing horizon is more than 4.5 km, the high gas factor is more than 250 m3 / ton, the focal development system, restriction of the control capabilities of the pressure maintenance system, the proximity of the nature protection zone. The presented characteristics of the field lead to a number of risks that are realized in the manifestation of the chain of complications, such as: uneven drop in reservoir pressure, the appearance of local zones of degassing, the breakthrough of injected water, an increase in the frequency of the change of submersible electric pump equipment, the fall of HDAS in BHZ and segments of the oil collector and pipes etc. Thus, the increase in the number of measures to prevent and eliminate associated complications leads to an increase in operating costs. In the materials of the work the key areas of the concept "Intelligent Field" are highlighted, the complex implementation of which allows qualitatively improving the reliability of calculations and evaluating the effectiveness of the current system of development and productivity of the technological chain. These results clearly show the wide possibilities of using the implemented systems and the potential for achieving quantitative results, such as the reduction in daily fund downtime and monthly losses in oil production, improving the quality of the planned operational regime of the wells, targeted increase in the degree of influence on the reservoir system, increasing employee productivity in the analysis and forecasting of production and injection levels. Synergy effects allows to minimize the risk of additional transaction costs without compromising technological safety. The presented work makes a certain contribution not only to strengthening the methodological knowledge of the oil industry community, but also to developing a set of examples with successful results of approbation of the approaches to intellectualization of the asset, useful as "benchmarking" in the oil and gas industry.

Gas condensate fields present unique challenges regarding data acquisition, data quality, exception-based surveillance, flow modeling, nodal analysis, well testing, allocation, and visualization. Although existing tools and methods address many of these aspects, it is possible to streamline processes and explore increased production efficiency methods. This paper addresses these challenges; it presents a case study of an intelligent control system implementation for a gas-condensate field based on a unified data model, integrated modeling, and cross-domain workflows. This paper presents a transformative, intelligent, and automated work process, referred to here as "smart workflows." As part of these workflows, virtual gauges are used that are based on inflow models and lifts, adjustable valves, and modular networks. The workflows are implemented on a truly open end-to-end platform that enables the coupling of multiple databases, streamlining of data for an integrated analysis of the measurements and model calculations, and ascertaining the mismatch between the two. The workflows also initialize adaptive self-tuning procedures. The smart workflows enable engineers to achieve various improvements, including an integrated structure of process data model to enable quick access to validated data, monitoring and control functions to a gas-condensate field in real time, and reduced downtime and operational costs. The smart workflow also supports functions that include collection and verification of measurement data, configuration of the integrated solution component models, evaluation of the action of root causes, and planning of operation scenarios. As part of the implemented system, an integrated information system data structure sets the degree of relatedness of tasks, each of which can be initialized depending on work situations and/or operator commands. Such comprehensive analysis of the data provides reliable integrated system configuration parameters of the model, which increases the accuracy of the calculations used in the optimal planning of the operational scenarios.

In introduction, will be given the short history of maturity field from 1976 year, main challenges, road map of implementing Smart Field (SF), current raediness. The main difficulties of implementing SF are depended on two factors: 1) Accuracy of tuning the components of integrated asset model-reservoir, wells and gathering networks by utilizing real-time data from SCADA systems, 2) Accuracy of execution the plan of injection and production at a field by field staff, subcontractors. This paper is focused on the two very crucial workflows which are helped to connect the integrated asset model with reality and increase helpful of usage model based approach: 1) real-time data preparation for executing the well model matching procedure and 2) maintaining operational regime for production and injection wells which is calculated by integrated asset model. Each workflow is organized as a sequence of steps which can be executed manually especially for the very complex well behavior or in an automated mode in simple case. The novelty of that experience includes the usage schemes and principals for mentioned routines: 1) special order of preparing data for further matching procedure of well/net/reservoir models, and readiness additional sets of transient data for further interpretation procedures 2) controlling execution process of maintaining the operational parameters at a field, generating some complex indicators show what to need to adjust models, measurement devices or submersible equipments.