A New Data Analytics Based Method to Characterize Waterflood Strategy in Geologically Challenging Mature Oil Field

The uncertainties associated with oil and gas field reduces with time. When oil fields mature, there is a potential to better understand the field due to the availability of historic production and injection data. In this research, a novel approach is presented which uses data analytics techniques to optimize waterflooding in a Gulf of Suez field. A combination of qualitative and quantitative techniques has been applied to develop a new workflow for analyzing and optimizing waterflood. The presented technique involves combining qualitative analysis (random forest) and quantitative analysis (capacitance resistance model, CRM) to obtain a waterflood strategy for the producing field. The Random forest algorithm (machine learning technique) is used to compare two time series signals – production data and injection data from producer/injector wells. The data from each injector and surrounding producers are used for random forest analysis to identify the most effective and ineffective injector-producer pairs. Next, the qualitative analysis using the capacitance resistance model (CRM) is used to determine gain values between each injector-producer pair and to also obtain new injection rates for increasing oil recovery. Results obtained from the random forest model helps reduce the number of unknowns and further validate results in CRM. The production and injection data reveal the most effective and ineffective injector-producer pairs that are the result of changes occurring in the reservoir during waterflood. Accordingly, the use of data analytics technique of random forest analysis and CRM on production injection data helps improve reservoir characterization. This combined analysis for the presented field uniquely helps identify effective and ineffective injector-producer pairs to determine the efficiency of waterflooding. The results from this novel analytical technique are presented for the Gulf of Suez field. These results compare well with the streamline approach presented for the same Gulf of Suez field. In summary, a new method for reservoir surveillance using data analytics technique of random forest in combination with the capacitance resistance model is presented. The novel combination of the qualitative and quantitative methods presented also helps adapt the specific characteristic of this field – the presence of water drive (pseudo injector). The modeling of water drive as an additional injector (pseudo injector) improves the gain coefficient obtained from the CRM. The comparison with streamlines helps benchmark the model results especially in cases where such secondary data is not available. The model presented can be adapted to similar mature fields under waterfloods. This new approach can be used to optimize water injection more frequently using operations data being gathered for implementing digitization strategies for oil and gas companies.