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Traditionally, the history matching process is done only on the dynamic model, without any direct update to the geological (or static) model. As a result, geological uncertainties are not fully evaluated in the dynamic model. Non-integration of static and dynamic modelling results in either too much time being spent modelling detailed geological phenomena that have little impact on the dynamic behaviour of the reservoir, or, conversely, important geological and petrophysical parameters being misrepresented or missed out which may have significant impacts on the overall field development strategy.
Ideally, if any updates to static parameters are required as result of history matching in the dynamic model, these changes should be reflected directly in the static reservoir model, thereby ensuring consistency between the static and dynamic models.
In this paper, a workflow is presented where both the static and dynamic modelling software packages are integrated as part of the history matching process. This workflow involves input parameters being adjusted in the geological model directly. Uncertainty analysis tools are used to obtain multiple history-matched models, which results in an order of magnitude increase in speed compared to traditional history-matching processes.
Not only will this methodology result in improved history-matched models with a wider range of production forecasts being captured, but more importantly, it will result in better understanding of the static and dynamic uncertainties and their interdependencies, leading to a more informed decision-making process with regards to overall field development. In addition, this methodology offers a platform where the subsurface professionals involved in reservoir model construction and simulation processes can focus their efforts on improving reservoir characterization and identify areas that require further data acquisition or improvement.
This paper also describes how the workflow was successfully applied to a recently developed, producing and waterflooded oil field in South East Asia, and eventually delivering an optimized reservoir model for reservoir management and a probabilistic approach to production forecasting.
This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 71596, "Trends in Reservoir Simulation: Big Models, Scalable Models? Will You Please Make Up Your Mind?" by Sheldon Gorell, SPE, and Robert Bassett, SPE, Landmark Graphics Corp., originally presented at the 2001 SPE Annual Technical Conference and Exhibition, New Orleans, 30 September-3 October.
Saudi Aramco Upstream data volumes have been exploding exponentially over the past few years. Reservoir engineers need to quickly analyze huge volumes of multidisciplinary data coming from multiple sources scattered across the upstream business, such as simulation, seismic, corporate database and real time data from intelligent fields. In addition, it is crucial to capture the scattered multidisciplinary experience that Saudi Aramco has across the upstream business. Multidisciplinary professionals are utilizing many expert systems that require specialized experience in their areas. It is very important to break these silos and bridge the gaps to capture all the scattered knowledge and experience in an integrated media to build shared understanding within multidisciplinary teams. Business intelligence provides a platform for guided processes and workflows that help to overcome these challenges.
The developed reservoir engineering business intelligence workflows will help engineers to analyze, visualize and report reservoir simulation results, production development scenarios and economics to enhance decision making process. This paper will discuss the Saudi Aramco methodology to develop reservoir engineering business intelligence workflows that utilize advanced data mining, visual analytics, and predictive analytics techniques. In addition, we will review several workflows that improve the process of history matching and prediction by rapidly identifying trends, anomalies, outliers and patterns in the reservoir simulation results.
The completion strategy and hydraulic fracture stimulation are the keys to economic success in unconventional reservoirs. Therefore, reservoir engineering workflows in unconventional reservoirs need to focus on completion and stimulation optimization as much as they do well placement and spacing. This well-level focus requires the integration of hydraulic fracture modeling software and the ability to utilize measurements specific to unconventional reservoirs. This paper details a comprehensive integration of software, data, and specialized measurements specific to unconventional reservoirs that allows efficient full-cycle seismic-to-simulation evaluations.
It is very important to properly model hydraulic fracture propagation and hydrocarbon production mechanisms in unconventional reservoirs, a significant departure from conventional reservoir simulation workflows. Seismic-to-simulation workflows in unconventional reservoirs require hydraulic fracture models that properly simulate complex fracture propagation which is common in many unconventional reservoirs, algorithms to automatically develop discrete reservoir simulation grids to rigorously model the hydrocarbon production from complex hydraulic fractures, and the ability to efficiently integrate microseismic measurements with geological and geophysical data. The introduction of complex hydraulic fracture propagation models now allows these work-flows to be implemented.
This paper documents an efficient, yet rigorous, integration of geological and geophysical data with complex fracture models, single-well completion and stimulation focused reservoir simulation, and microseismic measurements. The implementation of a common software platform and the development of specialized gridding algorithms allow complex hydraulic fracture models to be calibrated using microseismic measurements in the context of local geology and structure. The complex hydraulic fracture geometry, including the distribution of proppant, is automatically gridded to a common Earth Model for single-well reservoir simulation.
The software platform, newly developed complex hydraulic fracture models, and automated gridding algorithms are illustrated in a case history from the Barnett Shale unconventional gas play.
We present an innovative workflow for reservoir characterization, gridding, discretization, and simulation of discrete fractures embedded within a single-porosity continuum. This discrete fracture modeling (DFM) workflow provides the capability to realistically model the impact of fractures on recovery without incurring the simplifying assumptions of traditional dual-porosity models. We represent the matrix of the unstructured DFM using 3D polyhedral cells including tetrahedra, pyramids, and prisms. The fractures are represented as polygonal interfaces between matrix cells.
The workflow is enabled by a next-generation reservoir simulator that is designed for robust solution of unstructured grid models. The simulator supports parallel computation on distributed and shared memory machines without manual user intervention. The efficiency of the simulator is due primarily to a parallel linear solver that is designed specifically for unstructured grids. This results in a very efficient and robust reservoir simulator for highly heterogeneous DFMs.
We demonstrate the practicality of this technology by performing a design of experiments (DoE) study that involves a suite of sector models representing multiple discrete fracture realizations of an actual carbonate reservoir. We also show the feasibility of a full-field simulation including over ten thousand discrete fractures, more than a hundred wells, and 3.65 million simulation cells. These simulations are challenging due to the high contrast in matrix and fracture properties, multiphase flow, and the inclusion of complex physics (e.g., rapid gravity segregation in fractures, first- and multi-contact miscibility).
The successful application of the DFM workflow is largely due to the availability of the next-generation technology for reservoir modeling. We could not perform this work using existing simulation technologies designed for structured grids. Our DFM work facilitated the understanding of recovery mechanisms and contributed to business decisions toward optimal development of the carbonate reservoir.