Machine Learning Advanced Algorithm to Enhance Production Optimization: An ANN Proxy Modelling Approach
Authors: Cadei Luca , Rossi Gianmarco , Montini Marco , Fier Piero , Milana Diletta , Corneo Andrea , Lancia Lorenzo , Loffreno Danilo , Purlalli Elisabetta , Carducci Francesco , Nizzolo Riccardo , Sophia Gustavo 
Published: January 2020

Oil&Gas industry is still facing a conjuncture, characterized by high uncertainties and a subsequent investments reduction. This leads to an ever-increasing attention towards more efficient exploitation and production optimization projects. The current paper highlights the development and results of an innovative methodology able to generate high accuracy and fast simulation models through advanced analytics and deep learning techniques. These models are then implemented in a pioneering integrated production optimization tool, supporting field operations.

Industrial physical simulators are at upstream industry core, as they are involved in a consistent number of key processes and analysis. Regarding production optimization studies, they often represent the real bottleneck, as a single simulation can take up to several minutes or hours to converge. In this paper, a proprietary evolutionary algorithm is used as production optimization tool. It searches for the best variables combination amongst several thousand alternatives, triggering several simulator runs. In this scenario, machine learning techniques such as neural networks have proven to be extremely effective in learning how to replicate the physical simulator results and processes.

Proxy models have been leveraged to replicate two types of simulators: Fluid-dynamics for well/gathering network and Thermo-dynamic for process treatment. In both cases, one fundamental pre-requisite before training the neural network is to gather enough training samples, covering uniformly the multidimensional input space. In order to explore properly the possible input domain, operating ranges have been defined for all input variables. A random walk learning algorithm has been developed, based on the definition of boundaries, similarity functions and repulsion rules. As a result, tens of thousands of input-output tuples have been quickly generated. To replicate simulators, a number of different regressors have been compared, from gradient boosting to decision trees with neural networks, consistently producing the smallest errors. Covering uniformly all the input space dimensions ensure that the network can easily learn nonlinearities, avoiding the risk of overfitting. In order to make this innovative architecture fully effective and efficient, a highly flexible model training and validation framework as well as a scalable micro-services structure have been developed. Consequently, it has been possible to build proxy models learning the behavior of any physical simulators, proving the proper amount of input-output tuples, and to plug it in any complex processes, analyzing any possible scenario.

Unplugging the physical simulator from the production optimization pipeline and substituting it with proxy models resulted in an overall 103-104 time reduction, and a mean error smaller than 1% meaning high accuracy. This critical time reduction, unlock the full potential of what-if and production optimization analysis through evolutionary tool used, giving the possibility to optimize the production system on-line and real-time facing every different operating scenario and maximizing the value of the asset.