The main goal of history matching is to calibrate a reservoir model to production data and provide a model on which field development decisions can be made. Most of the history matching effort conducted in the industry is based on a single deterministic simulation model. With recent developments in probabilistic history matching techniques, faster computers, and efficient numerical algorithms, the opportunity to exploit these resources for efficient history matching workflows becomes available. In this paper we present a combined approach of assisted history matching followed by Brownfield Design of Experiment (DoE or ED) for model development for the Tengiz field. The Tengiz oilfield, located along the northeastern margin of the Caspian Sea, is the world's deepest producing supergiant oil field. The underlying simulation model of the field uses a dual-porosity, dual-permeability compositional formulation. The first step in the workflow was performing sensitivity study using one variable at a time analysis (OVAT) to reduce the number of uncertainties. This was followed by several Design of Experiments cycles to minimize the model misfit in static bottomhole pressures and MDT pressures and to modify the uncertainty ranges for the uncertain variables impacting the history match. The history match for the Tengiz field was achieved without any local modifications. The next step in the workflow was simulating the Brownfield DoE to create probabilistic reservoir models. The objective of this step was to develop multiple history matched models that generate a range of prediction outcomes. Two sets of prediction scenarios were considered: a base case which included all business plan work and a future development case. 203 DoE cases were run for each of the development scenarios. All DoE cases were used to generate proxy equations for the pressure mismatch functions (relative error and L2-norm) and cumulative oil production for the base case and the future development case. Multiple proxies were used to select probabilistic reservoir models while maintaining a good history match. The resulting probabilistic models will be used for reserves estimations, production optimization from existing infrastructure, design of future projects (ongoing drilling and expansion of an existing miscible flood), and to assess any future opportunities.
The Tengiz oilfield is a giant, carbonate reservoir of Devonian to Carboniferous age located in the Pricaspian Basin of the Republic of Kazakhstan. Gas Injection at the Tengiz field began in January 2007 with sweet gas injection as Phase 1 of the Sour Gas Injection (SGI) pilot project followed by sour gas injection as Phase 2. Phase 2 injection began in October 2007, but was interrupted due to initial start up problems. Continuous sour gas injection was achieved three months later in January 2008.The SGI project has four signposts for success: compressor reliability, injectivity, wellbore durability, and reservoir performance. The sour gas compressor at Tengiz was the first of its kind and has had greater than 90% availability when the Second Generation Plant (SGP) has been operational (SGI injection gas is produced through SGP). Injectivity has exceeded expectations and wellbore durability has also been excellent. Reservoir performance is a longer term signpost which is monitored though an extensive surveillance program. The Tengiz Reservoir appears to be performing as expected to the sour gas injection.The SGI project is a first-contact miscible gas injection process consisting of seven inverted five-spot patterns. To expedite data acquisition, the SGI well patterns were designed to include one "super-spot" pattern (twin injectors 100 m apart providing dedicated injection support to different geologic layers) and three short-spaced producers (producer-injector spacing approximately 1/3 of the standard spacing). Tracers, pulse tests, multiphase meters, gas saturation logs, and production and injection logs are used to monitor and understand reservoir performance. A specialized simulation model (the SGI Monitoring Model) was constructed which uses local grid refinement in the SGI pattern area. This model is used to determine how well the reservoir characterization is able to capture the dynamic reservoir response to the miscible SGI process.An earlier paper (Darmentaev et. al., 2010) discussed preliminary results from the SGI project. The proposed paper will discuss recent results from the reservoir surveillance program, how these recent results compare to the preliminary results, and the integration of all results into our current understanding of SGI performance. In addition, updates to the Monitoring Model, lessons learned and best practices developed since the commencement of sour gas injection will be discussed.
Tengizchevroil (TCO) operates two giant carbonate oil fields, Tengiz and Korolev, located on the northeast shore of the Caspian Sea in Kazakhstan. Both fields are Middle Devonian to Upper Carboniferous isolated carbonate platforms. Since 1991, the fields have produced around 2B barrels of oil. As more geologic and dynamic data becomes available, an updated history match of the existing dynamic field models is required to provide more accurate simulation results for estimating reserves, optimizing production and assessing future field development opportunities. In this paper, we present a case study on the use of brown-field design of experiments (DoE) on dual-porosity and dual-permeability Korolev field simulation model. Korolev is a highly fractured reservoir, and the physics of fluid movement is mainly controlled by the fracture network. In order to capture the uncertainty in the extent of fracture region at Korolev, discrete low, mid and high fracture models were created. The history matching process was split into three separate DoE studies, one for each of the fracture models, to obtain good quality proxies. After history matching the three fracture models separately, a combined proxy was created, and the final probabilistic P10, P50, P90 models were selected from the suite of all low, mid and high fracture extent models. The history matching workflow consisted of selecting uncertainty parameters and ranges which honor geological data, identifying parameters having high impact on the history match quality, conducting brown-field DoE of historical and prediction periods, developing proxies for EUR objective functions and history match mis-match functions, and model selection. The majority of history matching effort was spent on the static well pressure match, followed by minimizing the modular dynamic test (MDT) pressures, production logging tool (PLT) profiles and water cut mis-match functions. Approaches in key areas which helped to improve the quality of Korolev history matched models will be discussed in detail. The history matching workflow with low, mid and high fracture models described in this paper is believed to be superior to approaches that use a single fracture realization with fracture porosity, fracture permeability and sigma (fracture-matrix interaction term) history match modifications within the simulator. This is because the use of three fracture realizations allows for adjustment of intrinsic fracture properties (fracture density, aperture size, fracture extent, etc.). In addition, the use of three separate DoE studies allows for more accurate proxy model development.
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