You, Junyu (Petroleum Recovery Research Center) | Ampomah, William (Petroleum Recovery Research Center) | Sun, Qian (Petroleum Recovery Research Center) | Kutsienyo, Eusebius Junior (Petroleum Recovery Research Center) | Balch, Robert Scott (Petroleum Recovery Research Center) | Cather, Martha (Petroleum Recovery Research Center)
In this paper, a hybrid scheme that couples artificial neural network (ANN) and multi-objective optimizers is structured to co-optimize oil recovery and carbon storage of CO2 - EOR processes. The workflow is developed and validated employing an injection-pattern-based model. A field scale case study is presented to demonstrate the practicability of the workflow.
An injection-pattern based reservoir model employing a compositional numerical simulator is established to develop and test the hybrid-optimization workflow. Such a scheme aims at optimizing objective functions including oil recovery factor, CO2 storage and project net present value (NPV). An ANN expert system is trained and employed as a proxy of the high-fidelity model in the optimization process. The ANN model is trained by a robust optimization procedure which is competent to find the best architecture. Particle swarm optimization (PSO) is coupled with the developed proxy model to optimize a weight-aggregated objective function, and multi-objective functions by a Pareto front approach. A field case study is included in this paper. The reservoir model is well-tuned via a rigorous history matching process using the available field data. The aforementioned workflow is deployed to optimize the tertiary recovery stage of the field development.
In this paper, the validation results of the proxy model will be compared against results from the high-fidelity numerical models. Investigations focus on comparing the optimum solution found by the aggregative objective function and the solution repository (Pareto front) generated by the multi-objective optimization process. The optimization results provide significant insight to the decision-making process of CO2 - EOR project when multiple objective functions are considered.
This study develops a novel hybrid-optimization workflow for CO2 - EOR projects considering multiple objective functions. The robustness of the development is confirmed via a field case study. Moreover, this work investigates the relationship between the solutions of the aggregative objective function and the Pareto front, which provides constraints and reduces uncertainties involved by the multi-objective optimization process.
You, Junyu (Petoleum Recovery Research Center) | Ampomah, William (Petoleum Recovery Research Center) | Kutsienyo, Eusebius Junior (Petoleum Recovery Research Center) | Sun, Qian (Petoleum Recovery Research Center) | Balch, Robert Scott (Petoleum Recovery Research Center) | Aggrey, Wilberforce Nkrumah (KNUST) | Cather, Martha (Petoleum Recovery Research Center)
This paper presents an optimization methodology on field-scale numerical compositional simulations of CO2 storage and production performance in the Pennsylvanian Upper Morrow sandstone reservoir in the Farnsworth Unit (FWU), Ochiltree County, Texas. This work develops an improved framework that combines hybridized machine learning algorithms for reduced order modeling and optimization techniques to co-optimize field performance and CO2 storage.
The model's framework incorporates geological, geophysical, and engineering data. We calibrated the model with the performance history of an active CO2 flood data to attain a successful history matched model. Uncertain parameters such as reservoir rock properties and relative permeability exponents were adjusted to incorporate potential changes in wettability in our history matched model.
To optimize the objective function which incorporates parameters such as oil recovery factor, CO2 storage and net present value, a proxy model was generated with hybridized multi-layer and radial basis function (RBF) Neural Network methods. To obtain a reliable and robust proxy, the proxy underwent a series of training and calibration runs, an iterative process, until the proxy model reached the specified validation criteria. Once an accepted proxy was realized, hybrid evolutionary and machine learning optimization algorithms were utilized to attain an optimum solution for pre-defined objective function. The uncertain variables and/or control variables used for the optimization study included, gas oil ratio, water alternating gas (WAG) cycle, production rates, bottom hole pressure of producers and injectors. CO2 purchased volume, and recycled gas volume in addition to placement of new infill wells were also considered in the modelling process.
The results from the sensitivity analysis reflect impacts of the control variables on the optimum results. The predictive study suggests that it is possible to develop a robust machine learning optimization algorithm that is reliable for optimizing a developmental strategy to maximize both oil production and storage of CO2 in aqueous-gaseous-mineral phases within the FWU.