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This paper presents the implementation of an integrated reservoir modeling approach that tightly connects and integrates different reservoir modeling disciplines. The approach allows the propagation of subsurface reservoir uncertainties across the various modelling domains, from seismic interpretation though to dynamic reservoir simulation and surface facilities modeling. The results achieved by this approach is a geologically consistent ensemble of runs (100 runs or more) that matches observed data and capable of predicting the future field performance under various development scenarios with a higher degree of reliability. Using an ensemble of runs that first honor the geological facies, as initially defined in the prior probability field, and subsequently updated by reservoir production behavior in the post probability field, enables the user to predict future field performance by allowing hydrocarbon recovery probabilities to be calculated, where the impact of subsurface reservoir uncertainties on prediction results and the possible risk in each development decision is estimated. The field studied is located Offshore in Abu Dhabi and has four main, two secondary, and few minor stacked carbonate reservoirs. Available evidence indicates that the field's reservoirs are not in communication and this study focuses on the main and secondary units. The conceptual geological model proposes that all the zones are conformable, with no truncation or pinching, forming a layer cake depositional model in which reservoirs range in thickness, from few feet in thin zones, to tens of feet in thick zones. Over time the field has been affected by different tectonic stress regimes, resulting in complex strike slip faulting, extending vertically across all reservoirs. Quantification of static and dynamic uncertainties together in the applied methodology is improving the team integration and common understanding for the field structural and geomodelling uncertainties and its impact on the dynamic model behavior for different reservoirs. The methodology has been tested where new drilled well data is included at different times and it showed an easy and quick update for the entire workflow from seismic to simulation. The model was calibrated to historical observed data using different geological uncertainties (structural, facies, geomodelling and dynamic uncertainties) which helped to achieve a geologically consistent and reliable models.
This paper presents the implementation of an integrated reservoir modeling approach that tightly connects and integrates different reservoir modeling disciplines. The approach allows the propagation of subsurface reservoir uncertainties across the various modelling domains, from seismic interpretation though to dynamic reservoir simulation and surface facilities modeling. The results achieved by this approach is a geologically consistent ensemble of runs (100 runs or more) that matches observed data and capable of predicting the future field performance under various development scenarios with a higher degree of reliability. Using an ensemble of runs that first honor the geological facies, as initially defined in the prior probability field, and subsequently updated by reservoir production behavior in the post probability field, enables the user to predict future field performance by allowing hydrocarbon recovery probabilities to be calculated, where the impact of subsurface reservoir uncertainties on prediction results and the possible risk in each development decision is estimated. The field studied is located Offshore in Abu Dhabi and has four main, two secondary, and few minor stacked carbonate reservoirs. Available evidence indicates that the field's reservoirs are not in communication and this study focuses on the main and secondary units. The conceptual geological model proposes that all the zones are conformable, with no truncation or pinching, forming a layer cake depositional model in which reservoirs range in thickness, from few feet in thin zones, to tens of feet in thick zones. Over time the field has been affected by different tectonic stress regimes, resulting in complex strike slip faulting, extending vertically across all reservoirs. Quantification of static and dynamic uncertainties together in the applied methodology is improving the team integration and common understanding for the field structural and geomodelling uncertainties and its impact on the dynamic model behavior for different reservoirs. The methodology has been tested where new drilled well data is included at different times and it showed an easy and quick update for the entire workflow from seismic to simulation. The model was calibrated to historical observed data using different geological uncertainties (structural, facies, geomodelling and dynamic uncertainties) which helped to achieve a geologically consistent and reliable models.
Economic evaluation of exploration and production projects ensures a positive return for asset operators and stakeholders and evaluates risk in field development decisions related to both reservoir model uncertainties and fluctuations in oil and gas prices. Traditionally, such evaluation is performed manually and deterministically using single or limited number of cases (limited number of reservoir models and few values of economic parameters). Such traditional approach does not integrate seismic-to-simulation reservoir model uncertainties, the reservoir model used is often unreliable due to inconsistent property modifications during the history matching process, full span of prediction uncertainty isn't properly propagated for economic evaluation and the whole process is not fully automated. This paper presents an integrated and automated forward modelling approach where static and dynamic models are connected to integrate the impact of uncertainties at the different modelling stages (seismic interpretation through geological modelling to dynamic simulation and further to economic evaluations). The approach is demonstrated using synthetic 3D model data mimicking a real North Sea field. It starts by building an integrated modelling workflow that can capture the various reservoir model uncertainties at different stages to automatically generate multiple probable model realisations. Proxy models are constructed and used to refine the history match in successive batches. For each prediction development scenario, prediction probabilities are estimated using posterior ensemble of geologically consistent runs that matches historical observed data. The ensemble of reservoir models is automatically evaluated against different possible economic scenarios. The approach presents a seamless and innovative workflow that benefits from new-generation hardware and software, enables faster simultaneous realisations, produces consistent and more reliable reservoir models. Probabilistic economic evaluation concept is implemented to calculate the statistical probabilities of economic indicators.
Naturally fractured reservoirs are holding most of the hydrocarbon proven reserves worldwide. Field development planning and optimization of fractured reservoirs face significant challenges due to fracture system complexity, high reservoir heterogeneity and multiple recovery mechanisms. Fractured systems generally have multiple sets of fractures in different orientations, multiple apertures ranging from tiny fissures to large fracture conduits resulting in complicated fluid flow. Reservoir modeling plays a key role in field optimization by examining reservoir recovery under various scenarios. Traditionally, modeling fractured reservoirs is a deterministic process using a single or few reservoirs model (high, mid, and low cases) without proper integration of seismic-to-simulation reservoir model uncertainties. The full span of possible reservoir models and prediction uncertainty isn't captured nor propagated to economics. This paper presents an integrated forward modeling workflow using a Synthetic 3D model data mimicking a real North Sea field, where static and dynamic models are tightly connected to integrate the impact of uncertainties at different modeling stages (horizon uncertainties, fault uncertainties, petrophysical uncertainties, discrete fracture network (DFN) uncertainties to dynamic simulation).
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