We demonstrate how modelling decisions for a giant carbonate reservoir with a thick transition zone in the Middle East, most notably the approach to reservoir rock typing and modelling the initial fluid saturations, impact the hydrocarbon distributions and oil-in-place estimates in the reservoir. Rather than anchoring our model around a single base case with an upside and downside, we apply a comprehensive 3D multiple deterministic scenario workflow to compare-and-contrast how modelling decisions and geological uncertainties influence the volumetric estimates. We carry out a detailed analysis which shows that the variations in STOIIP estimates can be as high as 28% depending on the preferred modelling decision, which could potentially mask the impact of other geological uncertainties. These models were validated through repeated and randomised blind tests. We hence present a quantitative approach that helps us to assess if the static models are consistent in terms of the integration of geological and petrophysical data. Ultimately, the decision which of the different modelling options should be applied does not only influence STOIIP estimates, but also subsequent history matching & forecasts.@en

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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A particular challenge inherent to carbonate reservoirs is reservoir rock typing which impacts model initialisation and saturation distributions and hence STOIIP, phase mobilities, and flow behaviours. We explore how flow diagnostics can be used best to detect subtle differences in reservoir dynamics arising from different model initialisations by comparing flow diagnostics simulations with full-physics simulations. Flow diagnostics are applied to two reservoirs, a synthetic but realistic model representing an analogue for the Arab-D formation and a giant carbonate reservoir from the Middle East. Saturation modelling and reservoir rock typing is based on uniform and heterogeneous Pc and kr distributions, and further employs a state-of-the-art software that integrates of SCAL data and log-derived saturations. Sweep efficiency and dynamic Lorenz coefficients are then derived from the flow diagnostics results to quantify and compare the dynamic behaviour of the reservoir models. The full-physics simulations, which are used to validate the flow diagnostics results, are carried out with a commercial Black Oil simulator. The flow diagnostics results can clearly distinguish between different homogenous and heterogeneous rock-type distributions, wettability trends, as well as novel saturation modelling approaches that use dedicated software tools. Flow diagnostics capture the same trends in recovery predictions as the full-physics simulations. Importantly though, the total CPU time for a single flow diagnostics calculation including model loading is on the order of seconds, compared to minutes and hours for a single full-physics simulation. These observation give confidence that flow diagnostics can be used effectively to compare and contrast the impact of reservoir rock typing, saturation modelling, and model initialisation on reservoir performance before running full-physics simulations. Flow diagnostic hence allow us to reduce the number of reservoir models from a model ensemble and select a small number of diverse yet realistic reservoir models that capture the full range of geological uncertainties which are then subjected to more detailed reservoir simulation studies. Flow diagnostics are particularly well suited for complex carbonate reservoirs which are geologically more complex than clastic reservoirs and often exhibit significant uncertainties. Giant carbonate reservoirs are also challenging to simulate using full-physics simulators due to their size, so the impact of geological uncertainty on the predicted reservoir performance is often underexplored. Flow diagnostics are hence an effective complement to quantify uncertainty in state-of-the-art reservoir modelling, history matching and optimisation workflows, particularly for giant carbonate reservoirs.

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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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We apply, evaluate and compare several reservoir rock typing (RRT) approaches for a giant carbonate reservoir in the Middle East that is characterized by poor rock quality and a thick transition zone. These workflows are used to define RRTs by comparing several approaches, namely pseudo clouds from porosity and permeability data for each subzone, global hydraulic elements, petrophysical grouping using MICP data and Lucia classes. Then, the permeability models obtained from each RRT approach are used as input data for building the water saturation models using two different approaches, Leverett J-functions and the Geo2Flow software. The application of this workflow leads to a significantly change of the saturation distribution and subsequently revised the oil-in-place estimation for the field, especially because the reservoir has a thick transition zone. The variations in saturation distributions and oil-in-place arising from the different RRT approaches are likely larger than the uncertainty in the geological data itself. The saturation distributions do not only impact oil-in-place estimates but also predictions for the subsequent reservoir simulation models.@en

We apply a new and geologically consistent workflow to re-evaluate the saturation distribution in a giant and complex carbonate reservoir from the Middle East that comprises a thick transition zone. The workflow makes use of all the available capillary pressure, porosity, and permeability, data, irreducible water saturations, Archie exponents and cementation exponents. By using these data, the new workflow enables us to model hydrocarbon distributions more robustly. Hence the resulting 3D saturation model honours saturation logs at the wells exactly and allowed us to identify new reservoir compartments, leading to improved estimates of hydrocarbon reserves, and an estimated change in reserves of over 20%. The workflow is embedded in the Geo2Flow software and can be run conveniently from Petrel. The new saturation distribution needs to be considered as a new model scenario for any subsequent dynamic reservoir simulations.

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