This study investigated the feasibility of coupling a subsurface numerical reservoir simulator (POWERS) with a surface network modeling simulator, to assist in making better field management decisions according to business need. Coupled simulation models have two advantages over uncoupled models. First, interdependence of the reservoir and surface facilities are properly modeled in coupled simulation models to accommodate rapid variations in production strategies. Coupled simulation models are likely to give more accurate production forecasts compared with modeling the reservoir or the surface separately. Second, given that most surface network modeling tools have a built-in optimizer, it is possible to allocate rates among wells based on a user's objective optimizing function, -e.g., reducing or maintaining a watercut level for a given production target -taking into consideration any system production constraints applied on a well, a group of wells or trunkline levels. To improve the quality of simulation results, a new algorithm is implemented in POWERS to calculate the inflow performance relationship (IPR), based on drainage pressure, i.e., a reservoir pressure calculated as the average of several neighboring cells in the simulation model as opposed to the single cell pressure. The current study shows that it is feasible to run coupled POWERS-surface network models and gain the benefit from the optimization algorithm of the surface network modeling tool.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractProper field management for optimal performance of hydrocarbon reservoirs must capture the interdependence of the subsurface reservoir behavior and surface facility constraints. In this work we describe how full coupling improved development of a Saudi field by reducing water production by 30% while maintaining the target plateau for the required period of time.This was achieved by an iterative procedure that was able to devise an optimal producing strategy. The strategy involved time-dependent well production/injection rate allocations in response to field behavior. The strategy devised take into account production network constraints, network bottlenecks/under-utilization, and reservoir engineering complexities in producing three different reservoirs that make up the field.

High resolution reservoir modeling is necessary to analyze complex flow phenomena in reservoirs. As more powerful computing platforms become available, reservoir simulation engineers are building larger and higher resolution reservoir models to study giant fields. Large number of simulations is necessary to validate the model and to lower uncertainty in prediction results. It is a challenge to accurately model complex processes in the reservoir and also efficiently solve high resolution giant reservoir models on rapidly evolving hardware and software platforms. There are many challenges to get high performance in reservoir simulations on constantly evolving computing platforms. There are constraints which limit performance of large scale reservoir simulation. In this study, we review some of these constraints and show their effects on practical reservoir simulation. We review emerging computing platforms and highlight opportunities and challenges for reservoir simulation on those platforms. It is anticipated that management of data locality by the simulator will become very important on emerging computing platforms and there will be needs to manage locality to achieve good performance. Heterogeneity in the computing platform will make it difficult to get good performance without adoption of a hybrid parallelization style in the simulator. In this study, we analyze many benchmark results to illustrate challenges in high performance computations of reservoir simulation on current and emerging computing platforms.

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It is important to employ a good production injection strategy to optimize hydrocarbon recovery from a field. Reservoir constraints on the surface facility change as the field matures. It may be economical to revise the surface facility configurations rather than retaining the initial design of the surface facility to maintain the target production level of the field as reservoir conditions change. If reservoir pressure is not sufficient to maintain natural flow, there may be need for artificial lift mechanisms to keep the well flowing. Careful analysis of interdependence of the surface facility constraints and reservoir conditions are important in designing good quality production injection strategies for these circumstances. Coupled facility and reservoir simulations allow production optimization and determination of the impact of injection or disposal policies on reservoir management. Sometimes there are oscillations in computed production rates, which may be as a result from inaccurate treatment of a coupling algorithm, well management rules, optimization techniques, etc. Excessive fluctuations make the solution impractical to implement. In this paper, we examine the cause of well rate fluctuations in coupled simulations. We were able to keep oscillation levels at a very low level in our simulations by using a small coupling interval and by revising well management rules. We made case studies with reconfiguration of surface facility designs in two large fields to examine if those designs were capable of meeting the required production targets as the reservoir conditions change with time. Surface facility models were built using a commercially available software, which were coupled to Saudi Aramco's in-house reservoir simulator, POWERS. We examine scenarios where some facilities could be eliminated to reduce cost while maintaining required production target. We study the impact of reconfiguring the surface network and examine how surface constraints might change the overall production rate.

Assisted history matching (AHM) methodologies provide a systematic approach to history match reservoir models accounting for uncertainties. It also provides sensitivity of reservoir response within the uncertainty range of parameters. There are usually large degrees of uncertainties in a simulation model, and as the simulation model becomes very large, both engineering and computational complexities associated with AHM methodologies become massive. The performance an AHM algorithm depends on its ability to provide a solution with an acceptable level of accuracy and uncertainty tolerance and computational efficiency to reach that goal. This study provides performance evaluation guidelines for AHM studies and a cost benefit metrics for feasible history matching studies of giant simulation models. These metrics will take into consideration several criteria, such as the quality of the simulation model, the requirement for compute and storage resources, time to converge to an optimal or acceptable simulation model, user friendliness and ease of integration of the tool in an existing simulation environment. The goal of this evaluation metrics is to assist reservoir engineers to identify the best class of tools and algorithms, which will be appropriate for history matching studies of simulation models. The evaluation matrices were used to evaluate two stochastic tools. One is utilizing genetic/evolutionary algorithms and the other one is using different global statistical algorithms. The study is performed using an oil field in Saudi Arabia. This study identified key strengths and shortcomings of these two classes of algorithms for large scale history matching studies. The paper demonstrates that the current metrics can serve as a suitable screening tool to identify an appropriate methodology to be used in a history matching study.

Streamlines provide snapshots of flow patterns in the field, which simulation engineers can use to shorten the history match cycle when validating reservoir models. They can also help engineers develop injection strategies and improve the sweep efficiency by analyzing flow patterns and by estimating injector to producer relationships in the field during computations of various prediction scenarios. Our strategy in this study is to use details of reservoir conditions obtained by traditional reservoir simulations and calculate streamlines from the computed flow field. Saudi Aramco's in-house developed simulator POWERS and its post processing environment have been enhanced to generate streamlines from computed reservoir flow fields. A streamline tracing software developed at Texas A&M University and POWERS has been customized to generate streamline outputs. The algorithm used for computations is parallelized to run on Linux clusters. Several features, such as computations of injector/producer allocation factor and injector efficiencies, have been implemented in our streamline generation tool kit. In this paper, we present two case studies to illustrate advantages of streamlines when used in a workflow along with reservoir simulations to improve water flow managements of the field. Our analysis resulted in an optimal design with a fewer number of injectors to maintain the production target. In addition, streamline analysis helped us to find optimal well locations for injectors.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractProper field management for optimal performance of hydrocarbon reservoirs must capture the interdependence of the subsurface reservoir behavior and surface facility constraints. In this work we describe how full coupling improved development of a Saudi field by reducing water production by 30% while maintaining the target plateau for the required period of time.This was achieved by an iterative procedure that was able to devise an optimal producing strategy. The strategy involved time-dependent well production/injection rate allocations in response to field behavior. The strategy devised take into account production network constraints, network bottlenecks/under-utilization, and reservoir engineering complexities in producing three different reservoirs that make up the field.