An application of evolutionary algorithms for WAG optimisation in the Norne Field

Water-alternating-gas (WAG) is an enhanced oil recovery method combining the improved macroscopic sweep of water flooding with the improved microscopic displacement of gas injection. The optimal design of the WAG parameters is usually based on numerical reservoir simulation via trial and error, limited by the reservoir engineer’s availability. Employing optimisation techniques can guide the simulation runs and reduce the number of function evaluations. In this study, robust evolutionary algorithms are utilized to optimise hydrocarbon WAG performance in the E-segment of the Norne field. The first objective function is selected to be the net present value (NPV) and two global semi-random search strategies, a genetic algorithm (GA) and particle swarm optimisation (PSO) are tested on different case studies with different numbers of controlling variables which are sampled from the set of water and gas injection rates, bottom-hole pressures of the oil production wells, cycle ratio, cycle time, the composition of the injected hydrocarbon gas (miscible/immiscible WAG) and the total WAG period. In progressive experiments, the number of decision-making variables is increased, increasing the problem complexity while potentially improving the efficacy of the WAG process. The second objective function is selected to be the incremental recovery factor (IRF) within a fixed total WAG simulation time and it is optimised using the same optimisation algorithms. The results from the two optimisation techniques are analyzed and their performance, convergence speed and the quality of the optimal solutions found by the algorithms in multiple trials are compared for each experiment. The distinctions between the optimal WAG parameters resulting from NPV and oil recovery optimisation are also examined. This is the first known work optimising over this complete set of WAG variables. The first use of PSO to optimise a WAG project at the field scale is also illustrated. Compared to the reference cases, the best overall values of the objective functions found by GA and PSO were 13.8% and 14.2% higher, respectively, if NPV is optimised over all the above variables, and 14.2% and 16.2% higher, respectively, if IRF is optimised

Numerical Modeling of Multi-mechanistic Gas Production from Shale Reservoirs

Shale and ultratight gas reservoirs have recently been contributing to the energy industry and gas market to a large extent. The dynamics of shale gas transport in porous media is of practical importance in several scientific and engineering applications. The characteristics of the transport inside the pore space are governed by the mechanisms that occur at the pore level. Recent advances in computational power provide the opportunity to investigate these phenomena further. In this study, a methodology is developed to create a model in which all the major transport mechanisms involved in shale gas flow are taken into account. The mechanisms include viscous flow, gas slippage, Knudsen diffusion, competitive adsorption of different gaseous components, pore size variation and real gas effect. The model is then utilized on one hand to derive parameters such as apparent gas permeability and matrix-fracture fluid exchange term (a.k.a. shape factor) which can reduce the computational load while preserving the accuracy and on the other hand to study the response of shale gas reservoirs to the feasibility and potentials of carbon storage and enhanced gas recovery as well as phenomena such as nano-confinement and chromatographic separation. The compositional effects of shale gas can be lumped into a single component using the apparent permeability which deems to capture the relevant physics and can replace the Darcy permeability. The shape factor required for Darcy scale simulation of shale gas reservoirs obtained from the detailed numerical simulations of multi-mechanistic multi-component shale gas flow can be modeled versus dimensionless pressure to capture the transient behavior of the matrix-fracture fluid transfer in a time-independent fashion. The stronger adsorption of CO2 over CH4 to shale surface makes the partially depleted shale gas reservoirs a promising target for CO2 storage as well as enhanced natural gas recovery. Up to 55% of the injected CO2 can be trapped as adsorbed phase and up to 16% incremental methane recovery can be achieved. The phase behavior of the confined shale gas is significantly different than the behavior of the bulk fluid. Nano-confinement could shift critical properties significantly. The effect of confinement on phase diagrams and compositional variations of the gas in place was also investigated via numerical simulations. The computed apparent permeability and shape factor can be directly used in the macroscale reservoir simulators to accurately predict the performance of a shale gas reservoir and the outcomes of this study will find applications in the design and implementation of an efficient CO2 injection and investigating compositional effects in shale gas simulations

Carbon dioxide compressibility factor determination using a robust intelligent method

Owing to the demanding applications and wide uses of supercritical carbon dioxide in oil, gas and chemical industries, fast and precise estimation of carbon dioxide compressibility factor is of a vital significance in order to be imported into the relevant industrial simulators. In this study, a data bank covering wide range of temperature and pressure was gathered from open literature. Afterwards, a rigorous novel approach, namely least square support vector machine (LSSVM) optimized with coupled simulated annealing (CSA) was proposed to develop a reliable and robust model for the prediction of compressibility factor of carbon dioxide. Reduced temperature and pressure are the inputs of the model. 80% of the dataset was used for training the model and the remaining 20% was used to evaluate its accuracy and reliability. Statistical and graphical error analyses have been conducted to investigate the performance of the model and the obtained results from the proposed model have been compared with those of six equations of state, REFPROP package and two correlations. It was demonstrated that the proposed CSA–LSSVM model is more efficient and reliable than all of the studied empirical correlations, equations of state and the software package, hence it can be utilized confidently for the prediction of carbon dioxide compressibility factor

Prediction of centrifuge capillary pressure using machine learning techniques

In current literature in the petroleum industry, machine learning has been used to predict capillary pressure only on the centrifugal data points and not the complete capillary pressure curves generated from existing correlations after analysis. This paper will present novel information that will benefit the petroleum industry as it shows machine learning techniques can be used to obtain the complete capillary pressure curve which is the end goal in undertaking an SCAL centrifuge experiment. This research involves testing core samples using a centrifuge set up to produce capillary pressure data points. Then, using a commercial SCAL interpretation software, the collected data is utilized to generate complete capillary pressure curves based on developed literature correlations. RCAL data for the core samples is also obtained to be used with the machine learning techniques. The machine learning models are then applied to the collected data to predict the capillary pressure curves. Optimization of the different machine learning techniques is done to improve the predictions. The results show the machine learning techniques perform very well on the validation set after being trained on the training set. The machine learning models also provide reasonable prediction of the complete capillary pressure curves on the testing data set. Changing of the machine learning technique parameters also shows the effect on the overall precision and the improvements that can be made. Further research can be done to see the effectiveness of using machine learning techniques to predict other SCAL properties such as relative permeability. This can then greatly reduce the time needed to obtain these extremely important properties for reservoir characterization