Real-Time Steam Allocation Workflow Using Machine Learning for Digital Heavy Oil Reservoirs

Abstract

Thermal oil recovery processes are widely used to extract bitumen and heavy oil. Traditionally, a predetermined amount of steam is allocated to various injector wells using reservoir model based open-loop optimization. This practice can face a number of constraints including interruptions in well operations and/or surface facilities. Given that steam supply costs are a significant contributor to the overall production cost of heavy oil, dynamic and intelligent allocation of steam to various wells in the oilfield deserves further attention. In this study, we propose a proactive steam allocation workflow that can learn the effect of steam injection pattern on heavy oil recovery by using machine learning. We employ data analytic predictive models for the short-term forecast of the key performance indicators (KPIs). Model parameters are updated continuously by using a moving horizon approach that considers selected prior data including real-time measurements. An objective function containing predicted KPIs is maximized by manipulating the amount of steam allocated to various injectors in the oilfield. The workflow is repeated on a daily basis for continuous optimum steam allocation. A case study is performed by using a 3D reservoir model that represents a segment of the steam-assisted gravity drainage (SAGD) operation. For each well, the polynomial model is identified in the time-domain to forecast KPIs. The effectiveness of the proposed method is evident from the results as NPV is increased by almost 25% – 50% compared to the base case with a constant steam injection pattern in all cases studied. Due to the efficient use of available steam, the steam-to-oil ratio is reduced significantly. An adaptive and flexible steam supply is also honored by the proposed workflow, ensuring maximum efficiency of the oil recovery process. Practical implications of the proposed intelligent steam allocation workflow will be consequential in improving the operational efficiency of the digital heavy oil assets, thereby increasing profits and reducing the carbon footprint.

History Matching and Production Prediction of Steam Drive Reservoir Based on Data-Space Inversion Method

“…As a data-driven method, machine learning can solve any reservoir simulation problem with input-output link and provide reservoir prediction quickly by building learning model through a large number of data training [16]. Sibaweihi et al [17] employed learning models for the shortterm forecast of NPV with regard to the redistribution of steam. The model parameters are updated continuously by using a moving horizon approach that considers selected prior data including real-time measurements.…”