... various types of production and surveillance data at high frequency. Since the data represents the current status of the reservoir, data analytics can be performed to create the data-driven models to predict the economic performance indicators (EPIs) for the short term. The decision variables can...

... studies evaluated the type of scales collected from plugged sand screens; however, more investigation is required to address the reservoir conditions and wellbore hydraulics affecting the scaling potential of minerals at downhole conditions. A laboratory workflow combined with a predictive modeling...

... is not productive as confirmed by 4D seismic due to poor near-wellbore geology. With the modified P50 realization, a successful match within 5% was achieved. Well Pair D Well Pair A From the plots, the model's predictions matched reasonably well with the historical performance at 3 years. In the field...

... geologically consistent history matching training image prediction hamdi geologic modeling Artificial Intelligence history matching probability perturbation method facies distribution society of petroleum engineers SAGD process reservoir model Introduction Reservoir interpretation, modelling...

... increased by only a modest amount to 27.7%. The disagreements were somewhat larger when the new method was used to make pure predictions of two published data sets. Accordingly, it was concluded that the new approach can be used to make reasonable predictions (possibly within a factor of 2) of solvent–oil...

... are relevant for the prediction of H 2 S production in the context of steam injection. bitumen enhanced recovery oil sand SAGD thermal method Modeling & Simulation compositional kinetic model prediction reservoir simulation ayache kinetic model rock conductivity Upstream Oil & Gas...

... of limited number of network elements, ignoring the resistance of pore elements, multi-scale structures, and tight/weak connections represented by an inadequate number of voxels. Pore network extraction negatively affects the accuracy of petrophysical predictions and ignores solid matrix and its thermal...

... algorithm approach is adopted to perturb the shale indices until the difference between the target production history and the production history predicted from the AI model is minimized. A number of cases have been tested. The results show a good agreement between the shale barrier configurations predicted...

... Abstract The steam-assisted gravity drainage (SAGD) method is an efficient way of producing oil from many of Canada's Oil Sands reservoirs. Predicting oil production and steam injection rates is required for planning and managing a SAGD operation. While this can be done by simulating the fluid...

..., with a focus on the production rate and the steam-oil ratio (SOR). Then the trained AI proxy model was used to predict SAGD production profiles for scenarios where the shale barriers were distributed stochastically. The results of these predictions were compared with the results of the SAGD simulation model...

... neural network (ANN) is employed to facilitate the production performance analysis. Predicting (input) variables that are descriptive of reservoir heterogeneities and operating constraints, including log-derived petrophysical parameters, dimensionless shale index, effective numbers of producers...

.... constant temperature steam-solvent combination Upstream Oil & Gas n-pentane weight fraction viscosity constant pressure experiment n-pentane bitumen mixture solvent weight fraction Alberta Weight Fraction fraction enhanced recovery concentration prediction bitumen different temperature...

... and t α +1 (see Appendix for more details). Abstract The present study proposes a novel single-layer Neural Network proxy to efficiently predict the production performance of oil reservoirs from a limited number of reservoir simulations. The proposed model is shown to provide powerful...

... performance prediction. Predicting (input) variables including porosity, net-to-gross ratio, saturation, gross pay, normalized shale barrier thickness and distance to well pair, and initial production rate are formulated. Measures such as cumulative production over discrete time intervals are considered...

... behavior predictions for water/n-alkane mixtures by use of the Peng-Robinson equation of state (PR EOS) with the van der Waals mixing rules. Binary interaction parameters (BIPs) are first optimized for water with n-alkanes in terms of three-phase curves including upper critical endpoints (UCEPs), where...

..., artificial neural network (ANN) is employed to predict SAGD production in heterogeneous reservoirs, an important application that is lacking in existing literature. Numerical flow simulations are performed to construct a training data set consists of various attributes describing characteristics associated...

... (ES-SAGD) and hybrid solvent, requires accurate predictions of phase equilibria and the mutual solubility of different solvents in bitumen. Despite the importance of accurate phase behaviour predictions, there is a lack of fundamental and mechanistic knowledge relevant to bitumen/solvent equilibrium...

... machine learning flow in porous media Artificial Intelligence steam-assisted gravity drainage modeling Simulation prediction SAGD performance Reservoir Characterization SAGD Fluid Dynamics Thickness shale barrier length scale permeability cumulative oil production Upstream Oil & Gas...

... chambers are also observed as shown in Fig. 2 and Fig. 3 . Mathematical Formulation and Solution In our research, we propose a new analytical model for predicting steam chamber development process and SAGD production performance. There are three main features compared with previous research: (1...

... with the characterization scheme of treating heavy oil as a single pseudocomponent, the absolute average relative deviation (AARD) for the predicted saturation pressures can be improved from 5.27% to 4.56% by characterizing the heavy oil as six pseudocomponents. With the optimum BIPs, the swelling factors are predicted...