20FORCE Olsen A Machine Learning Approach to Optimizing the Allocation of Steam used in ConocoPhilli
Channel: Force Seminars
Date: 2018-10-02

Title: A Machine Learning Approach to Optimizing the Allocation of Steam used in ConocoPhillips Canadian Steam Assisted Gravity Drainage (SAGD) Project

Abstract:
Reducing the cost of emulsion production in the current cost environment is no longer merely prudent, but necessary. Steam generation is one primary cost driver in this process, and therefore maximizing the bitumen produced given available generated steam is important to reducing the cost of production.
As operational concerns may require curtailment of steam generation, or provide a temporary surplus capacity, the optimizing how steam allocation among wells requires a dynamic solution. Many traditional optimization techniques are not applicable to our practice as the target, measured produced emulsion at each well, is not continuously available despite having nearly continuous steam injection.
We employed machine learning techniques, using a supervised learning approach, to predict the produced emulsion on an individual well basis. The ‘ExtraTreesRegressor’ has been the most predictive algorithm for more than 60 process variables (Steam Flow, Pressures, Temperatures, Valve Positions, etc.) passed in. These predictions have then been used as the basis of optimization for steam allocation. We have validated several of our predictive models during field trials and are working towards air-gap testing of our model-based optimized recommendations. Despite the accuracy of the ExtraTrees predictions, they have created rectilinear solution spaces allowing non-unique optimal settings on a per well basis. We are working on other approaches using deep learning, namely with time series forecasting of the subsurface temperatures and pressures using Recurrent Neural Networks, to break this rectilinear for optimization purposes.

Christopher Olsen,