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.