Cyclic Steam Injection Modeling and Optimization for Candidate Selection, Steam Volume Optimization, and SOR Minimization, Powered by Unique, Fast, Modeling and Data Assimilation Algorithms
Authors: Sarma Pallav , Fowler Garrett , Henning Mark , Kyriacou Stylianos , Thakur Ganesh , Sloss Dakin , Shipley Wyatt 
Published: April 2017

Abstract
The application of a novel modeling and data assimilation approach is presented, demonstrating the impact of quantitatively modeled and optimized cyclic steam candidate and steam volume selection in a mature heavy oil field. Results are reviewed for a cyclic steam operation in the San Joaquin Basin in California, including steam savings, production increases and SOR reduction in excess of 20 percent.
The new approach is based on a novel modeling and data assimilation method. This paper focuses on the data assimilation aspect of the workflow, which is fast, quantifies uncertainty and enables simultaneous assimilation of multiple data sources. The approach is a combination of a modified Ensemble Kalman Filter (EnKF) with quadratic programming that enables assimilation of data from thousands of wells and different data sources with a relatively small ensemble. Additionally, since the EnKF is known to underestimate uncertainty, statistical techniques are used to correct the uncertainty estimates to conform with empirical estimates. The predictive capacity of the calibrated models is demonstrated through a statistical back-test process, wherein, the wells are fitted to historical data up to before the last steam job, and the response of the last steam job is predicted and compared to actual production. Once a calibrated model is validated, the models can then be used to predict the performance of future jobs, and thereby identify the best wells to steam, and also optimize steam volumes that minimize SOR and maximize incremental oil production due to the steam job.
The above described modeling and optimization workflow has been applied to multiple fields in the San Joaquin valley. For the field chosen for this case study, production comes from the unconsolidated sands of the Pliocene Chanac and Kern River formations with porosity averaging 30%, permeability averaging 1,500-2,000 mD and net thicknesses typically between 50 and 300 feet. Dip is generally monoclinal across the field at approximately 5 – 15 degrees. The reservoir is shallow, with depths ranging from 750 – 2,250 feet. Oil gravity averages 14 – 16° API. Reservoir pressure is well below bubble point and averages 25 – 100 PSI. Data sources assimilated included production and injection rates, wellhead pressures, steam and producer temperatures, temperature profiles, steam ID logs and various petro-physical logs. Results to date indicate that application of this workflow has increased incremental oil production from the steam jobs by over 44% compared to a prior control period. And most interestingly, such an increase in production is achieved while cutting overall steam injection, as the steam volume optimization identifies inefficient use of steam.
The most significant new finding is that cyclic steam injection can be quantitatively modeled and optimized rapidly to maximize profit and minimize SOR. This is important, particularly for mature heavy oil fields, in order to maximize recovery amidst varying commodity prices.
The novelty of the new model is its combination of speed of data integration (less than a week) and runtime (minutes) with long-term predictive accuracy (years or decades). This is due to the unique modeling and data assimilation methodology.