In this paper we demonstrate how key geological uncertainties in a giant onshore carbonate reservoir in the Middle East, most notably fracture permeability and saturation distributions, impact the quality of the history match and change the performance forecasts of a planned miscible water alternating gas (MWAG) injection process. Different geological models for the reservoir were designed by integrating static and dynamic data. These data indicated the need to consider fault-related fractures using effective medium theory (EMT) and to update the saturation distribution by integrating special core analysis and log-derived J-functions in the reservoir model during the history matching. Afterward, multiobjective optimization (MOO) was applied for each history-matched model to identify well controls that optimally balanced the need to maximize the time on the plateau rate while adhering to the field’s gas production constraints. Our results clearly show that including low-intensity fault-controlled fractures in the reservoir model improved the quality of the history match for the gas/oil ratio (GOR), BHP and water cut (WC). This is especially true for wells located near faults, which were difficult to match in the past. Moreover, our results further show that the updated saturation model improved the quality of the history match for the WC, and honored water saturation from the log with high accuracy, particularly for wells located in the transition zone. Applying MOO for each history-matched model then allowed us to identify well controls for the MWAG injection that could extend the time at which the reservoir would be produced at the plateau rate for up to 11 years and the risk of losing production plateau down to 2 years, while always adhering to the current field operational constraints. We demonstrate how the integration of MOO with an innovative workflow for fracture and saturation modeling impacts the prediction of a planned MWAG injection in a giant onshore carbonate reservoir. Our work clearly illustrates the potential of integrating MOO with new reservoir characterization methods to improve the quantification of uncertainties in reservoir performance predictions in carbonate reservoirs.

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An innovative multi-deterministic scenario workflow was applied to one of the giant and complex carbonate reservoirs in the Middle East. The application of this workflow had the objective to quantify how geological uncertainties and different modelling decisions impact the stock tank oil-initially-in-place (STOIIP) estimates and flow behaviour in this reservoir. In particular, we focused on the uncertainties related to the presence of fractures, reservoir rock typing, and modelling the initial hydrocarbon distribution. Based on the available static and dynamic data we considered two key scenarios, the absence of fractures and the presence of sparse, fault-controlled fractures. In the first scenario, we investigated how different reservoir rock typing methods impact permeability distributions. We further quantified changes in hydrocarbon distribution and analysed how a novel approach that combines capillary pressure and log-derived J-function affects the saturation models. In the second scenario, we used the effective medium theory to calculate permeability multipliers for the regions where fractures are expected. This enabled us to effectively represent fractures in a single-porosity reservoir model. The representativeness of the different models was analysed through blind tests using static data as well as history matching using dynamic data. The most significant findings of our work are that subtle changes in modelling decisions and reservoir rock typing have major consequences for the saturation model, leading to up to 20% change in STOIIP estimates. Such uncertainties must be carried forward in future reservoir management decisions and when estimating reserves. The blind tests showed that a saturation model based on the combination of core- and log-derived J-functions gave the most robust STOIIP estimates. These particular saturation models further led to a much-improved history match, especially for wells located in the transition zone of the reservoir. The best history matches were obtained once sparse, fault-controlled fractures were included in the reservoir model using effective medium theory. The presence of fractures specifically improved the history matching quality for wells located close to the faults; these wells were very difficult to match in the past. Our work clearly demonstrates that a multi-deterministic scenario workflow is key to explore the appropriate range of geological uncertainties, and that, equally important, the impact of different modelling decisions must be accounted for when quantifying uncertainty during reservoir modelling. This is particularly applicable to giant carbonate reservoirs where relatively minor changes in the workflow and data interpretation can have major consequences on STOIIP estimates, dynamic behaviours, and reserve estimates. Multi-stochastic modelling workflows which anchor the reservoir to a single base case are not capable of achieving this.

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We demonstrate how key geological uncertainties in a giant onshore carbonate reservoir in the Middle East, most notably fracture permeability and saturation distribution, impact the quality of the history match and change the performance forecasts of a planned Miscible Water Alternating Gas (MWAG) injection process. To achieve this, we used a history matching and multi-objective optimisation (MOO) workflow that was tightly integrated with an innovative reservoir modelling workflow that paid particular attention to the fracture and saturation modelling. Different geological models for the reservoir were designed by integrating static and dynamic data. These data indicated the need to consider fault-related fractures and to update the saturation distribution in the reservoir model. The effective medium theory was therefore used to estimate effective permeability in order to capture the presence of low-intensity fault-controlled fractures in the reservoir. The integration of Special Core Analysis (SCAL) and log-derived J-functions allowed us to build alternative saturation models that honoured well data with great accuracy. The resulting history matched models therefore accounted for the key geological uncertainties present in the reservoir. Afterwards, MOO was applied for each history matched model to identify well controls that optimally balanced the need to maximise the time on the plateau rate while adhering to the field's gas production constraints. Our results clearly show that including low-intensity fault-controlled fractures in the reservoir model improved the quality of the history match for the gas oil ratio (GOR), bottom hole pressure (BHP) and water cut. This is especially true for wells located near faults, which were difficult to match in the past. Moreover, our results further show that the updated saturation model improved the quality of the history match for the water cut, particularly for wells located in the transition zone. These different history matched models yielded different production forecasts, where the time at which the reservoir can be produced at the plateau rate varied by up to ten years. Applying MOO for each history matched model then allowed us to identify well controls for the MWAG injection that could extend the time at which the reservoir would be produced at the plateau rate for up to nine years and the risk of losing production plateau down to two years, while always adhering to the current field operational constraints. We demonstrate how the integration of MOO with an innovative workflow for fracture and saturation modelling impacts the prediction of a planned MWAG injection in a giant onshore carbonate reservoir. Our work clearly illustrates the potential of integrating MOO with new reservoir characterisation methods to improve the quantification of uncertainties in reservoir performance predictions in carbonate reservoirs.

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Water-Alternating-Gas (WAG) injection could be an efficient way to improve recovery factors in fractured carbonate reservoirs. However, there are many geological uncertainties and engineering designs that influence the efficiency of the WAG process. In this study, we explore the challenges of combining polynomial chaos expansion (PCE)-based proxy modelling with multi-objective optimization using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize the design of a WAG injection method under pseudo-miscible conditions for a fractured carbonate reservoir that is an analogue for the Arab D formation. A range of realistic geological uncertainties and engineering parameters are considered. Our results show that the order of the PCE-based proxy model needs to be carefully established for each geological scenario in a fractured reservoir. Likewise, the iterative enrichment of the proxy-model is non-monotonic, and each geological scenario requires its own evaluation of the proxy model during iterative enrichment. The convergence of the proxy is independent of the degree at which the reservoir is fractured but depends on the dimensions of the parameter space. The more engineering controls are considered, the more iterations are needed. Without a continuous and careful evaluation of the proxy model, the resulting simulation estimates may deviate significantly from full-physics simulations.@en

Water-Alternating-Gas injection could be an efficient way to improve recovery factors in fractured carbonate reservoirs. However, the complex geology of fractured carbonate reservoirs and the complexity of the WAG process itself create uncertainties when predicting the efficiency of WAG. It is hence challenging to identify engineering controls that allow us to optimize recovery during WAG while accounting for geological uncertainties. To overcome this challenge, we use Latin Hypercube and Box-Behnken experimental designs to set up multiple screened simulations of WAG injection in a fractured carbonate reservoir model that contains multiple geological uncertainties. From these simulations we construct surrogate response surfaces using polynomial chaos expansion. The response surface allow us to predict recovery factor, gas cumulative production, water cumulative production and NPV for different geological scenarios and engineering controls. Finally, we run the Non-dominated Sorting Genetic Algorithm-II on the response surface to obtain an optimal engineering design that not only accounts for geological uncertainties but also competing objectives. Our results show that only such a comprehensive simulation approach, which nevertheless can still be carried out on modern desktop PCs, is capably of analysing the complex interactions between geological and physical uncertainties and engineering controls to identify the most suitable WAG implementation.@en