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Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm's efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir. [ABSTRACT FROM AUTHOR]

Numerical models representing geological reservoirs can be used to forecast production and help engineers to design optimal development plans. These models should be as representative as possible of the true dynamic behavior and reproduce available static and dynamic data. However, identifying models constrained to production data can be very challenging and time consuming. Machine learning techniques can be considered to mimic and replace the fluid flow simulator in the process. However, the benefit of these approaches strongly depends on the simulation time required to train reliable predictors. Previous studies highlighted the potential of the multi-fidelity approach rooted in cokriging to efficiently provide accurate estimations of fluid flow simulator outputs. This technique consists in combining simulation results obtained on several levels of resolution for the reservoir model to predict the output properties on the finest level (the most accurate one). The degraded levels can correspond for instance to a coarser discretization in space or time, or to less complex physics. The idea behind is to take advantage of the coarse level low-cost information to limit the total simulation time required to train the meta-models. In this paper, we propose a new sequential design strategy for iteratively and automatically training (kriging and) cokriging based meta-models. As highlighted on two synthetic cases, this approach makes it possible to identify training sets leading to accurate estimations for the error between measured and simulated production data (objective function) while requiring limited simulation times. [ABSTRACT FROM AUTHOR]

Multi-stage hydraulic fracturing in horizontal wells has been proven an effective stimulation method to enhance well performances from extremely low permeable reservoirs by creating a complex fracture network. The concept of stimulated reservoir volume (SRV) was introduced to quantify the complex fracture network. Knowledge of the SRV is important for the evaluation of the hydraulic fracturing performance and the estimation of hydrocarbon recovery. However, it is still a challenging topic to model the evolution of the SRV and estimate its properties because of the presence of fluid–solid complex interactions during the fracturing process. Most current SRV estimation methods are complex and computationally expensive. The paper applied a simple modified dilation-recompaction model to simulate the evolution of the effective SRV without representing the fracture geometry explicitly. By history matching production data of a multi-stage fractured horizontal well in Chang 8 formation of the YanChang oilfield, China, the calibrated dilation-recompaction model can successfully characterize the evolution of the generated SRV and estimate the rock properties of the fractured zones. The simulation results show that the total effective stimulated reservoir volumes amount to 88,375 m3. The shape of a fracture zone is nearly elliptical about 54 m wide and 20 m high surrounding a well perforation. The permeability of the zone reaches as high as 28 mD, centering around the wellbore and decreasing gradually outward. [ABSTRACT FROM AUTHOR]

Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm’s efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir.

Four-dimensional seismic (4DS) contains spatial information that provides insights into the location, shape, and movement of fluids (oil, gas, water). It helps engineers to adjust reservoir simulation models and increase their capability of providing reliable production forecasts. Recent probabilistic approaches consider hundreds of numerical simulation model scenarios, which require automated methods to evaluate this large number of numerical models based on observed 4D seismic data. Comparing spatial information of seismic and numerical simulation data is difficult as, usually, these data are converted to maps with different properties. We propose a novel approach to compare 4D seismic data and simulation models using a three-way deep neural network that is trained using a reference image (4D seismic data) with two simulation model candidates. It learns to find the simulation models that best characterize the reference. Our method is underpinned by more than a thousand pairs of simulation models and reference maps evaluated by human specialists for training. For testing, we compare the inter-rater agreement among different specialist groups and generate a reliable test set considering examples in which there was agreement among at least two specialists. We observed that the group with best-trained specialists agree more in their answers and have a considerably higher inter-rater agreement than the less trained groups. When we evaluate our method with the answers from this specialized group, we observe that the simulation model chosen by our method is the one agreed by the specialists in almost 90% of the cases. We also discuss the impact of different noise levels in the input and show that our method outperforms other approaches in the literature if noise is present both in the training and test sets. [Display omitted] • Novel strategy to compare simulated data from observed four-dimensional seismic (4DS) data. • Three-way deep neural network to obtain image representations. • 4D seismic and simulation data represented by a vector of main features. • Method robust to different scales, various domains, and noisy setups. [ABSTRACT FROM AUTHOR]