Steam-Assisted Gravity-Drainage (SAGD) is one of the most effective ways of recovering heavy oil. Injection of cold solvent into the SAGD formation is an enhancement to SAGD recovery technology to reduce the energy consumption and heat loss. SAGD process is a high energy-intensive process where it requires a high amount of steam to mobilize and recover the heavy oil. Besides, generation of steam requires the combustion of natural gas which adversely impacts the economics of SAGD, especially when the price for natural gas is high. Furthermore, large amounts of injected steam are retained in the reservoir where it reduces the thermal efficiency of the process. Therefore, this research aims to develop better recovery process with lower energy and emission intensities. In this study, a thermal compositional simulation was carried out using a commercial simulator to examine the performance of cold solvent in assisting heavy oil recovery. In this process, the cold solvent is injected into a mature heavy oil field where it has been recovering using SAGD process. The solvent will then utilize the energy retained in the reservoir to vaporize and spread within the steam chamber. The low temperature of the solvent will cool down the temperature of steam chamber where it enhances the solubility of solvent in the oleic phase and accelerates recovery. Carbon dioxide is chosen as the solvent for this process as it is considered as a lower-cost alternative compared to hydrocarbons. The simulation shows a secondary peak oil rate due to the cold solvent, followed by a steady increase in oil production rate. This is also aided by the viscosity reduction due to carbon dioxide solubility. Steam injection is ceased when a cold solvent is injected into the formation, hence significantly reducing the steam requirement in heavy oil recovery. Overall results show that the cold solvent process increases the oil recovery by 2%, reduces steam-oil-ratio, reduces total heat loss by 16% and reduces steam requirement by 24%. Hence, the advantageous results from the simulation prove that cold solvent is a profitable and energy-efficient recovery method in recovering mature SAGD formations.
Oil prices see large fluctuations peculiarly over the last eight years due to natural disasters, political instability, and Covid-19 pandemic shock. These prompt to anxiety towards expenditure in planning and forecasting of a field development plan (FDP). Economic optimization of a reservoir under water drive can be extremely tedious and time consuming especially for complex field. Traditionally, upon completion of forecast optimization on fluid production, reservoir engineer willhand over the reservoir models to petroleum economist for economical evaluation. If the chosen development strategy is not economically viable, the model strategies will have to be updated, and continue the repetition of financial evaluation all over again. Hence, this paper established an automated workflow that diminished the dilemma on iterations obligation between simulation runs and financial reviews in searching for most efficient waterflooding strategy. The automated workflow is accomplished by bridging three tools together seamlessly utilizing python scripting. These include the cash flow economic spreadsheet model, the dynamic simulator, and an assisted uncertainty analysis tool. The process first started with defining the economic parameters such as OPEX, CAPEX, oil price, taxes, discounted rates, and other financial parameters on an annual basis in spreadsheet. The uncertainty parameters: water injection rate, maximum water cut, and injection duration will be evaluated during forecast optimization to produce project efficiency indexes: Net Present Value (NPV) and Benefit-Cost Ratio (BCR). This integration was achieved by python script that automatically creates a coding path which exchanges simulation production and economic spreadsheet data at every simulation time step and each development strategy, that require no manual intervention. The integrated economic-dynamic model workflow has successfully applied on West Malaysian field and Olympus model, a development strategy that maximize oil recovery without neglecting cost of water disposal, storage for total water produced from the reservoir. This paper successfully identified the most efficient waterflooding strategy and production constraints for each well using BCR as objective function for optimization. The optimum development scenario does have a BCR which is more than 2 which show that investment on that particular development strategy is profitable. The results also demonstrated a crucial impression that the highest oil cumulative production may not results in high BCR due to cost involvement in resolving water production and field maintenance services. This paper outlined the methodology, python scripting codes, and how integration automation works that successfully optimized an injection strategy in a development project using economic model from third-party application. The results of this automated workflow demonstrate a successful utilization of new technologies and simple customize programming knowledge that promote cross-discipline integration for enhanced work-time efficiencies in problem solving that is suitable for all reservoir model type to determine its success rate and economic viability during FDP.
Over the past decades, Assisted History Matching has been the new norm for history matching that leverages the rapid advancement in digital computational performance. Continuous advancements such as parallel computing and GPU accelerates numerical simulation which overcomes the cumbersome experience of working with large fine-scaled model that mainly concerns the simulation time and intervention of engineers. As more interest emerges around artificial intelligence in the optimisation process, this paper explores the Artificial Intelligence algorithm to optimize two proxy modelling techniques: Quadratic Polynomial and Artificial Neural Network proxy model. These techniques are compared with stochastic optimisation method known as Differential Evolution algorithm on their efficiency of optimizing the objective functions, time taken, and knowledge investment needed by engineer, given today's hardware technology. This paper starts off by using Latin hypercube experimental design to generate first ensemble of simulation cases to generate proxy models to match the historical cumulative oil and water production by well level. The quality of both proxy modelling techniques is evaluated using R2 coefficient and proxy plot. Proxy models are then further validated by creating real simulation models from variants generated via Monte Carlo Analysis. The history matching quality and practicality were compared between the AI algorithm that runs optimizer on top of existing proxy models, and Differential Evolution algorithm in optimizing the regional porosity and permeability multipliers. The ANN proxy model prevailed over quadratic proxies to mimic the numerical reservoir model output with high degree of accuracy. The black-box nature of the ANN proxies limits the interpretability of predicted model when compared quadratic proxies where the formula for the proxy model can be obtained. Quadratic approximations are more flexible, simplistic in nature, and requires less computational cost to be constructed. Despite that, its prediction quality maybe subjected to the degree of non-linearity in the simulation model. The use of AI algorithm vastly reduces the number of full reservoir simulation required to achieve the minimum objective function at a shorter timeframe, which is proved to be the strength of such method. However, AI optimisation is highly susceptible to be trapped in local minimum. This paper proved the superiority of Differential Evolution algorithm over AI, that it may avoid being trapped in local minimum to achieve high degree of prediction accuracy for the history matching given the larger number of iterations required. This paper provides a preliminary understanding of optimisation workflow and how to go about each optimisation strategies: quadratic polynomial proxy, ANN proxy, stochastic optimisation, artificial intelligence techniques, and a novel approach of converting proxy predicted variants into real simulation cases to evaluate proxy quality. Hence establishes engineers’ expectation by appraising the pros and cons of each optimisation strategies.
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