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Abstract Determining the most influential parameters affecting the reservoir flow responses is a vital step in the integrated reservoir studies for evaluation and analyses. More specifically, removing the non-influencing parameters leads to reach the optimal process design. The conventional procedure to determine the most sensitive parameters combines regression analysis with analysis of variance. However, that approach produces one reduced regression model after eliminating the non-influential parameters (deterministic approach). In this paper, Bayesian Model Averaging (BMA) was applied to stochastically identify the geological parameters that control the immiscible CO2-assisted gravity drainage process performance in a multilayer heterogeneous sandstone oil reservoir in South Rumaila oil field. After achieving acceptable history matching within approximately 57 year of production, the CO2-assisted gravity drainage injection was evaluated in 10 year future prediction. In that process, vertical wells were placed at the top of the reservoir for CO2 injection to formulate a gas cap that make oil drains down towards the bottom of reservoir. Above the oil-water contact, a series of horizontal production wells were installed to produce oil. The main geological parameters that controls the immiscible CO2 flooding are horizontal permeability, anisotropy ratio (Kv/Kh), and porosity that were investigated for their impact by layers or multilayer. Many low-discrepancy designed simulations were created by the Latin Hypercube Design and evaluated by the reservoir flow simulation to disregard the no-impact parameters using the linear BMA approach. Among multi- candidate models, BMA select the best model of optimal subset variables based on the highest posterior probability. BMA led to quantify accurate impact of each geological control on the CO2 EOR process. Based on the concept of the CO2-assisted gravity drainage process, it was concluded that horizontal permeability has a higher impact of the reservoir flow response than vertical permeability (Kv/Kh) with no effect of porosity.
Abstract The ACG Oilfield caps an elongate anticline with three culminations - Azeri, Chirag and Gunashli - and is located in the offshore Azerbaijan sector of the south Caspian Basin. This study focuses on Azeri in the south-east of the structure, which has over 8 billion barrels of oil in place. The major reservoir interval, the Pliocene Pereriv Suite, is characterized by laterally continuous layers of variable net-to-gross (NTG) deposited in a fluvial-deltaic environment. Azeri is being developed by down-dip water injection, with up-dip gas injection on the more steeply dipping central north flank. At the planned offtake rates both recovery mechanisms are expected to be stable. However, these predictions are based on reservoir models which do not explicitly capture the full range of geologic heterogeneity present in the Pereriv Suite reservoirs. We report the first detailed assessment of the impact of large- and intermediate-scale heterogeneities on flow. Experimental design techniques have been used to rank the impact of different heterogeneities. A key finding is that communication between adjacent high and low NTG reservoir layers significantly improves recovery, providing pressure support and a route for oil production from sandbodies within the low NTG layers which would otherwise be isolated. Heterogeneity within high NTG layers has only a small impact on recovery, but heterogeneity within low NTG layers is much more significant. In most cases, the same significant heterogeneities impact both water and gas displacements, because both displacements are stable at the planned production rates. The results are applicable to Azeri, and to similar reservoirs in the Caspian Basin. They also represent the first comparison of water-oil and gas-oil displacements in fluvial-deltaic reservoirs using 3D geologic/simulation models derived from outcrop and subsurface data. Introduction The giant Azeri-Chirag-Gunashli (ACG) Field occurs in a large, elongate anticlinal structure located in the offshore Azerbaijan sector of the south Caspian Basin (Fig. 1A). The structure has steeply dipping limbs and contains three culminations (Azeri, Chirag and Gunashli). This paper focuses on the Azeri accumulation in the south-east of the ACG structure, which contains an estimated 8 billion barrels of oil in place. The ACG field development project is one of the largest current energy projects (US$20 billion aggregate) in the world. The Azerbaijan International Operating Company, operated by BP, has been awarded a 30 year production license for ACG which expires in 2025, and plans to bring total production in ACG to 1 million bbls oil/day by 2008. The oil fields of the South Caspian Basin (Fig. 1A) have reservoirs in the thick (up to 7000 m) latest Miocene to early Pliocene strata of the Productive Series (Fig. 2A). These strata record multiple, high-frequency cycles of deltaic shoreline advance and retreat in response to fluctuating lake levels in the isolated South Caspian Basin 1–4 (Fig. 1B). The resulting Productive Series stratigraphy is strongly layered (Fig. 2A), and sandstone-bearing intervals are bounded by laterally extensive mudstones 1–4. Productive Series sandstones in the northern part of the South Caspian Basin (including the ACG Field) are quartz-rich, well rounded and well sorted, indicating deposition by the paleo-Volga River and Delta 5 (Fig. 1B).
Abstract For marginal field development and mature field re-development, the main art of maximizing reservoir contact is to design wells that could enable commingle production simultaneously depleting not only the major but also the selected minor sands in the field. Field implementation cases in Malaysia have been shown that this could significantly minimize the well count, increase the well productivity, and improve the ultimate recovery per well particularly in the multiple-stacked and compartmentalized reservoirs. Commingle production from several sands may have the risks and the uncertainties, among others, of layer cross-flow, excessive GOR production and early water breakthrough at certain sand intervals due to uneven pressure depletion, uneven gas and water mobility. These production risks and uncertainties shall be evaluated for ensuring the predicted life-cycle production performance of the designed commingles production wells. Minimization of these risks could involve developing of a pressure drawdown management plan, the optimization of injection fluid conformance control and the prediction of reservoir pressure change. The resulting pressure drawdown plan may then generate a requirement for individual down-hole flow control at each commingled sands. Accordingly, the smart completion comprises of inflow control devices such as passive ICD and/or active ICV with or without down-hole pressure and inflow monitoring devices namely, PDG or DTS installation can then be adequately designed. This paper is to illustrate a production integrated smart well completion design process starting from reservoir drainage and injection points selection, the determination of well reservoir contact trajectory, the production evaluation and risk analysis, and to the selection and application of smart completion devices. The case of a deepwater reservoir field development smart well completion design was used to demonstrate the viability of this integrated engineering approach. This approach is a partial effort to achieve effective field development by lowering the overall field development cost and maximizing the oil and gas recovery. The presented reservoir engineering workflows and completion design methodologies is to constitute a new smart well completion benchmark for well design and production optimization and serve as an engineering guide for optimizing the well construction cost in Malaysia. Introduction In Malayisa, oil and gas reservoirs can be classified into 3 main types, namely thin oil-rim, stacked, and compartmentalized reservoirs (Figure 1). Fields, shallow or deep, can have a structure which is the combination of these three types of the reservoirs. Field development often entails placement of the injection and production wells across several reservoir layers or zones of interest. Figure 2 shows a case of a horizontal well placement across several sands in a reservoir compartment bound by faults. In a deepwater reservoir with many stacked reservoir sands, a high angle well was designed for commingle production of 3 selected sands (Figure 3). These wells demonstrated that commingle production and injection from multiple sands could increase significantly the well productivity and EUR per wells, and could reduce the total well count for optimizing the field development.
Abstract The objective of this paper is to address the challenges that are frequently encountered in simulation studies when using local grid refine (LGR) within upscaled models. The difficulties mainly arise due to the unreliability of populating the fine grids with reservoir properties and attributes. Dynamic modeling of a pilot is an important task to predict fluid flow and reservoir behavior which is a major step of pilot design. Dynamic models usually have many limitations when it comes to geological description due to the upscaling of fine-grid static model. Using local grid refinement (LGR) alone for the pilot area within a coarse dynamic model also would not enhance reservoir description of the pilot area without a realistic reservoir description. This work was aimed to provide an improved method for the proper simulation of pilot project in order to optimize the design of the pilot injector, borehole location and length. Furthermore, the model would be used to plan an efficient reservoir monitoring program including an optimized well data gathering with sponge coring for defining the remaining oil saturation. To overcome these limitations, the proposed method introduces a special fine scale LGR covering the pilot area within the upscaled dynamic model. Whereas the upscaled model has 36 layers, the LGR contains exactly the same fine-scale layering scheme and reservoir properties as the static model with 160 layers. Thus, it eliminates the upscaling process within the LGR. This process will ensure a better quality history match, for example the TDTs and RSTs derived saturations when compared against the fine layer LGR from the dynamic model. The dynamic model in this study is a large sector model (Nx 167, Ny 98 and Nz 36) with a detailed LGR (Nx 130, Ny 145 and Nz 160). The fluid properties were calculated from a 10-components equation of state (EOS). After the addition of the LGR, the model history match was updated. Several prediction cases were then studied to optimize well location (injector and observation wells) in order to convert the existing inverted five-spot gas pattern into water injection line-drive. Several saturation maps and profiles were generated to predict the breakthrough time for each observer and utilized to design the future pilot monitoring program. The new data will be utilized for future updates of the history match and performance of pilot under the optimum scheme of sweep and thus well spacing.
Ghedan, Shawket G. (The Petroleum Institute) | Gibson, Adrian P. (Abu Dhabi Co. Onshore Oil Opn.) | Sener, Ilhan (Abu Dhabi Co. Onshore Oil Opn.) | Gunal, Ozgur Eylem (Petroleum Institute) | Diab, Alexander (Scandpower Petroleum Technology GmbH) | Schulze-Riegert, Ralf Wolfgang (Scandpower GmbH)
Abstract This paper presents a case study using a Multipurpose Environment for Parallel Optimization with application to assisted History Matching. Evolutionary Algorithms and deterministic optimization schemes are integrated into a workflow controlling a large number of parallel reservoir simulations. The practical applicability of a software assisted history matching process is illustrated on basis of a real case study. The history matching process is discussed for an inverted 5-spot water flood pattern that had been subject to an extensive program of reservoir pressure and water saturation monitoring. Results are analyzed and compared to traditional history matching to identify the potential added valued and increased efficiency. The quality of the manual history match was reproduced for pressure data and significantly improved for saturation data. The resulting model has proven to show better forward modeling characteristics defined by matching of some blind test data. Due to highly constraining boundary conditions defined by pressure and saturation data, it was not possible to generate multiple solutions to this specific problem. Within the given constraints, the model tuning parameters had to be close to the values of the reference case to be able to match such constraining pressure and saturation data. In conclusion, the software assisted history matching process was able to improve the quality of the match within less time and effort. A lesson learned process is discussed focusing on the engineer acquiring more information and improving the understanding on reservoir uncertainties and the reservoir model behavior. Introduction Geological models derived from static data, such as geological, well log, core and seismic data, often fail to reproduce the reservoir production history. History matching is defined as the process of reconciling geologic models to the dynamic response of the reservoir. The main purpose of history matching is building a numerical simulation model which is consistent with the entire available reservoir data, i.e. geological, petrophysical and SCAL data as well as production data including field and well pressure, flow rates, water cuts and gas oil ratios. In order to obtain an acceptable description of the reservoir by history matching, many different simulation runs in completely different regions of the search space must be performed. In order to capture reservoir model uncertainties within the range of model parameter uncertainties, a variety of models should be generated. They will not be distinguishable with respect to the reproduction of history data but may deliver different predictions of future reservoir performance. Manual history matching process often tunes limited set of parameters to reach to only one acceptable non-unique history matched model. In addition to this serious limitation, manual history matching is time consuming, and faces a real challenge in terms of keeping track of the model response to parameters changes and their combined effect on the field, sub reservoir and wells levels. Motivation Some known giant fields in the world are carbonate reservoirs characterized by a high level of heterogeneity. This level of heterogeneity necessitates the understanding of reservoir uncertainties in all levels of the reservoir modeling process including: data acquisition, geological static modeling and dynamic simulation modeling. Manual history matching of the simulation of these kinds of reservoirs often fails to cover important reservoir uncertainties. Due to the availability of increasing computing resources, software assisted optimization techniques with application to reservoir simulation are given more and more attention. With these techniques, turnaround times for creating new models and updating old models has been significantly reduced.