Estimates of recovery from oil fields are often found to be significantly in error, and the multidisciplinary SAIGUP modelling project has focused on the problem by assessing the influence of geological factors on production in a large suite of synthetic shallow-marine reservoir models. Over 400 progradational shallow-marine reservoirs, ranging from comparatively simple, parallel, wave-dominated shorelines through to laterally heterogeneous, lobate, river-dominated systems with abundant low-angle clinoforms, were generated as a function of sedimentological input conditioned to natural data. These sedimentological models were combined with structural models sharing a common overall form but consisting of three different fault systems with variable fault density and fault permeability characteristics and a common unfaulted end-member. Different sets of relative permeability functions applied on a facies-by-facies basis were calculated as a function of different lamina-scale properties and upscaling algorithms to establish the uncertainty in production introduced through the upscaling process. Different fault-related upscaling assumptions were also included in some models. A waterflood production mechanism was simulated using up to five different sets of well locations, resulting in simulated production behaviour for over 35 000 full-field reservoir models. The model reservoirs are typical of many North Sea examples, with total production ranging from c . 15×10 6  m 3 to 35×10 6  m 3 , and recovery factors of between 30% and 55%. A variety of analytical methods were applied. Formal statistical methods quantified the relative influences of individual input parameters and parameter combinations on production measures. Various measures of reservoir heterogeneity were tested for their ability to discriminate reservoir performance. This paper gives a summary of the modelling and analyses described in more detail in the remainder of this thematic set of papers.

Summary Faults are geological features that are essential in considering the development of hydrocarbon reservoirs. Significant resources (appraisal costs and manpower) are often deployed to locate them and assess their connectivity more accurately so that adequate investment decisions can be made. Early in a project's life, data might be sparse or the time that can be dedicated to data processing might be limited. An estimate of the impact of faults can be derived by considering the impact of analogous fault patterns in similar reservoir environments. A significant number of discoveries consist of channelized turbidite reservoirs draped over deepwater toe–thrust anticlines. To understand the effects of various fault patterns on the recovery factors of structurally complex turbidite reservoirs, we first perform extensive flow–simulation–based multidimensional sensitivity studies using realistic channelized stratigraphic architectures. Many of these reservoirs contain high–porosity, high–permeability channels, light oil, limited aquifer drive, and a limited number of fault populations. To constrain the potential impacts of the faults, a generic reservoir model was constructed and simulated with varying reservoir and fault properties. Because the input parameters are known (e.g., fault–zone thickness and permeability, shale–drape coverage, and oil viscosity), the relative importance of each variable can be quantified. The simulation results show that the impacts of the faults on reservoir performance vary systematically with the fault length and orientation, the undeformed–reservoir permeability, and the fault permeability. The recovery factors are relatively insensitive to fault–zone thickness, net/gross ratio, and porosity. The time–delay effect of the faults is significant in reservoirs with a permeability of approximately 100 md but not in darcy–quality reservoirs. In most fault scenarios, the recovery factors from simulations with high–permeability faults range from approximately 30% in 100–md reservoirs to approximately 45% in 3,000–md reservoirs, equivalent to 75 to 96% of the recovery factors of the unfaulted reservoirs. In comparison, assuming the most likely fault permeabilities, the recovery factors decrease to circa 20% in 100–md reservoirs and circa 41% in 3,000–md reservoirs, equivalent to 40 to 85% of the values in unfaulted reservoirs. To learn and predict the effects of faults on the recovery factor in a very fast and reasonably generic fashion in deepwater toe–thrust anticlines, we have developed an accurate deep–learning (DL) based surrogate model that predicts the structure connectivity factors as a function of recovery time. These factors are then used for discounting the recovery factors computed by simpler and easier–to–construct unfaulted models. The surrogate model is a very fast and advanced proxy for the multidimensional structure connectivity factor function and operates without resorting to any new flow simulation with a faulted model. We describe the novel DL architecture used for constructing the surrogate model and quantitatively prove its effectiveness. We finally demonstrate an example real–life application of the surrogate model on an exploration–stage prospect–ranking exercise.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractRecent successful exploration and production programs in deepwater settings worldwide have increased the need to better understand the range of structural and stratigraphic depositional complexities and their impacts on performance and sweep efficiency. As oil and gas companies explore and produce hydrocarbons in deeper water settings, and in their quest to produce hydrocarbons from difficult or unconventional reservoirs, there is a need to develop innovative ways of achieving success and reducing the overall cost -an enabler to remaining competitive and profitable.

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