Managing an oil and gas reservoir requires the integration and analysis of several data elements, including reservoir, well, and production data from multiple sources. Quick and efficient access to this data will allow engineers to concentrate their efforts on the important tasks of analysis and decision making, to improve reservoir and field performance. This paper presents an overview of the implementation of an automated workflow designed to support the management of a Saudi Arabian reservoir, by integrating real-time production data with other data elements. The workflow and techniques used require specialized engineering processes to fully understand the reservoir behavior and associated parameters to identify wells with anomalous behavior1. Among the solutions provided in this workflow is a system for real-time data retrieval, visualization, and analysis of reservoir production performance. This is achieved by linking dynamic surveillance processes with real-time data, and allowing data to be provided in real-time at the engineer's desktop. To provide a coherent explanation of the production behavior, multiple analysis processes and tools were used. These included a workover candidate selection system, decline curve analysis, and heterogeneity index analysis. The workflow also included mapping and quality control tools to flag out data outliers. In addition, alarm systems were included to alert the engineer when sudden changes in reservoir performance occur in the field. The implementation of this workflow has resulted in considerable time savings, with pertinent data being automatically updated and used in the analysis, as opposed to manual processing, leading to improved efficiency in field management practices. The workflow has been deployed as an application of the Intelligent Field concept in a carbonate field in Saudi Aramco.
Reservoir simulation is one of the main tools if not the most important one reservoir engineers use to forecast a reservoir performance. Nevertheless, developing and operating a reservoir simulator in the first place can be an arduous task that requires a set of highly skilled individuals in science, advanced mathematics, programing, and reservoir engineering and powerful computational models. v Table of Contents List of Figures .
Locating the water entries in an oil producer is critical for the success of remedial work, such as acid stimulation and water shut off, to prolong the life of the well and enhance production. Different logging tools are successfully used to obtain this information such as PLT. Practicality of using these logging tools can be limited. A good example of these limitations is a well that cannot flow or sustain flow to surface (dead well). Therefore, in addition to the historical data, previous PLTs, water drive mechanisms for such wells, along with the well performance history, the pressure gradient survey approach is used to come up with best estimate of static OWC in vertical or slightly deviated wellbores. This paper will answer the questions of when, and why conduct a pressure gradient survey and the optimum running procedure by discussing the utilization of the pressure gradient survey as a diagnostic and decision-making tool in a few case histories from the Ghawar filed in Saudi Arabia will be discussed. Introduction Locating oil water contact, OWC, in the wellbore is essential before any well intervention or remedial work such as acid stimulation and water shut-off. The current best tool for such information is the production logging tool, (PLT), which gives the flexibility of seeing both the dynamic, and static OWC, however running the PLT even though it seems as the only tool, is not always a cost-effective way. Therefore, in addition to the historical data, previous PLTs, water drive mechanisms for these wells, and the well performance history. The pressure gradient survey approach is used to come up with the best estimate of the static OWC in vertical or slightly deviated wellbores.
The shifting trend towards drilling multilateral wells has motivated engineers and researchers to investigate ways to improve their decision-making when designing multilaterals. The problem becomes more challenging in unconventional reservoirs, where tighter and highly heterogeneous and discontinuous reservoirs are targeted. Achieving the right design of a multilateral well configuration is a complex problem due to the vast possibilities of well forms (i.e., number, location, length and orientation of laterals) that need to be evaluated. Accordingly, arises the need for an inverse-looking approach to come up with a sound decision. Conventional ways in designing multilaterals involves investigation of different possible scenarios using a reservoir simulator model and comparing results against an objective function. However this addresses the problem only from a forward-looking point of view. This procedure is time and labor intensive and tremendously increases the computational cost. This paper illustrates the utilization of the artificial neural network (ANN) concept to develop an advisory system for designing multi-lateral well configurations for a given target reservoir performance in fraction of the time, cost and effort required by a numerical simulator. From that point onwards an engineer can improve on the recommended design or modify it to adhere to certain field, cost or operational constraints.
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