Sour gas injection (SGI) in the non-fracture platform area of the giant carbonate oil field, Tengiz, began in 2007. SGI project was proven to successfully maintain reservoir pressure in the platform area, add significant reserves, reduce sulfur production, and enable additional oil processing capacity at the crude processing facility. Despite the confirmed benefits, the gas breakthrough and increasing gas-oil ratio (GOR) trends in several SGI producers became a concern as the injection project matured. The preferential production from wells with lower GOR allowed higher total oil throughput, but also introduced production constrain on SGI wells with higher GOR. As the result, SGI producers were historically choked back or completely shut-in as soon as the gas breakthrough was confirmed and the producing GOR began to increase above 500m3/m3. The reservoir heterogeneity with the sour gas injection overprint created complex dynamic environment at the subsurface. Special surveillance program was designed to improve understanding of gas front movement through the reservoir, assess vertical and areal sweep efficiency and remaining oil in place in various zones of interest. Surveillance program design had to overcome several operational constrains, such as wellbore accessibility issues from the scale build, gas handling limits of the surface facilities, and complex simultaneous operations near the active high-pressure sour gas compressor. Moreover, the log interpretation had to consider crossflow and stimulation chemicals impact on the logging measurements. Finally, the integration of logging interpretation results with reservoir model was required to improve the reservoir model forecast and boost the value of acquired information. This paper describes the results of the conducted surveillance campaign, the novel calibration methodology of gas saturation profile from the time-lapse cased hole measurements with proxy from the multi-component simulation model output and the early results of the performed gas shut-off operations. The described methodology allowed direct calibration of the model outputs with the gas saturation results from pulse neutron logs and provided more accurate sweep efficiency and oil recovery forecast across the entire SGI area. Calibrated model revealed consistent gas breakthrough profile and significant volume of low GOR oil remaining in the wells with gas breakthrough. The updated reservoir model was then used to evaluate various development scenarios of SGI area. Gas shut-off scenario showed particularly encouraging low GOR production trends and improved oil recovery especially from the lower intervals. After the economic analysis, several wells, including long-term shut-ins, were added to the workover queue to timely realize production benefits. Early production results after gas shut-off workover consistently met or exceeded model forecasts. Described methodology provided more accurate scope definition, value assessment and justification for the SGI optimization project and could be applicable to other improved oil recovery projects.
Korolev field is a large Devonian-Carboniferous carbonate buildup with a flow system dominated by natural fractures. Currently TCO is looking into potential IOR opportunities at Korolev field, which might help to unlock additional resources beyond the scope of current development plans. Therefore, characterization and modeling of the fracture system is of fundamental importance for a new flow- simulation model to assess and predict IOR performance. The fracture modeling workflow closely integrates matrix and fracture modeling, which facilitates identification of important parameters for fracture distribution early in the modeling process. Fracture prediction is based on correlations with various geological parameters, such as stratigraphy, depositional facies, mechanical properties and geomorphological features, which provides a soft probability trend for distribution of fracture parameters. Fracture network characterization based on analysis of well log and core data only is very limited in scale. Pressure Transient Tests (PTT) and Pulse Tests provide important insights into characteristics of fracture network at the larger scale than the conventional wireline data allows. Therefore, it is important to incorporate dynamic dataset as a fracture characterization constraint during modelling of fracture distribution. Most of the wells at Korolev field have good quality pressure buildup and pulse test data. TCO developed a workflow to incorporate dynamic data into the fracture modeling process for the full- field dual porosity, dual permeability (DPDK) model. The first step in the workflow is to calibrate fracture density distribution to match well productivity indices (PI) observed in the field. The next step involves dynamic simulation of pressure buildup tests and their comparison to the actual measured data. The last step is to validate the geologic model with available pulse test data. Dynamic data integration required multiple iterations and loopbacks to fracture characterization and property distribution. Close collaboration between fracture experts, earth scientists and reservoir engineers along the whole process was essential for successful implementation of dynamic data into fracture characterization and modeling. Calibration with the available dynamic data led to better understanding of spatial distribution of fracture properties and provided important additional constraint for the fracture model construction. Improved fracture model at Korolev is the key factor for more reliable production forecasts and evaluation of future development opportunities.
Tengizchevroil (TCO) is the biggest operator in Kazakhstan developing two world's deepest supergiant oilfields - Tengiz and, its satellite field, Korolev. With over 20 years of oil production at TCO, reservoir pressure has been declining and is approaching bubble point pressure. In order to arrest the declining pressure trend and extend oil production plateau, TCO is evaluating Improved Oil Recovery (IOR) opportunities, including potential Waterflood in Korolev field. Accurate Waterflood evaluation requires improved characterization of the main uncertainties impacting ultimate recovery under IOR processes. Therefore, we built next-generation Korolev reservoir model (SIM15K) which incorporates results of the latest characterization efforts based on the latest wide- azimuth 3D seismic survey. This work led to updated Korolev depositional model, which helps to understand the links between geological settings and fracture occurrence. In conjunction with the first implementation of Dynamic Data Integration workflow, this resulted into updated Low-Mid-High fracture models - one of the main factors controlling Waterflood performance in naturally-fractured reservoirs. This paper focuses on Brownfield Experimental Design (ED) of Korolev field, which is specifically designed to provide an estimate of IOR Incremental Recovery. We identified 23 main uncertainty parameters for each Low-Mid-High Fracture models. The Brownfield ED was run with two development scenarios: Primary Depletion and Waterflood to get probabilistic assessment of Incremental Waterflood Recovery. Overall 803 cases were required for each fracture model and development scenario to generate good quality proxies for cumulative recoveries and History-Match error. Those proxies were used to sample the entire space of uncertainties and define P10/50/90 targets. As a result of robust Brownfield ED, we selected P10/50/90 models to capture both range in Incremental Waterflood Recovery and Ultimate Recovery under Primary Depletion. The underlying uncertainty parameters for the final model selection were picked based on their relative impact on the objective functions. Currently, the new SIM15K model is being used for Korolev Waterflood evaluation and optimization, Reserves estimation, existing infrastructure optimization and future projects design.
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