The objective of this paper is to scope the Chemical EOR potential in both GNPOC and PDOC fields in Sudan. From the initial EOR screening, the most amenable EOR processes identified for both GNPOC and PDOC are mainly chemical and thermal EOR. Chemical EOR is the leading EOR process in GNPOC fields while thermal EOR is the leading EOR process in PDOC fields. Chemical EOR evaluation was performed using Eclipse EOR black oil simulator. Simulations were performed on sector models constructed or extracted from full field models which have been conditioned to the current reservoir condition. The chemical input data was referenced mainly from Qing Hai oil field lab data which oil properties are similar to that of Sudan's. The chemical EOR evaluation encompass 3 different types of chemical processes; polymer flooding, surfactant-polymer (SP) flooding and alkaline-surfactant-polymer (ASP) flooding. Chemical EOR can potentially improve field recovery factor between 4-18% depending on the type of chemical EOR process. ASP flooding possess the highest potential with incremental oil recovery over waterflood ranging between 12%-18% followed by SP flooding and polymer flooding. ASP flooding is taken as the reference chemical process in this study as it as it represent the highest chemical potential. The outcome of this study is believe to be helpful to successful planning of Chemical EOR applications in sudan.
Waterflooding is one of the most widely implemented enhanced recovery in mature oil fields. In the absence of a reliable reservoir model, waterflood optimization can be a challenge. The availability of continuous recording of production, injection and well data can be utilized to improve reservoir management in this novel approach. This study presents a new approach using Machine Learning (ML) technique through multiple signal analysis to optimize waterflood operation in a brownfield offshore Caspian Sea. To evaluate injection efficiency on oil production, firstly the interwell connectivity between injectors and producers are determined. However, because of the complexities associated with the reservoir and the data, it has been achieved through analyzing various available signal types which are informative and responsive to injection rates. Results obtained from multiple signals are then aggregated to identify the injector-producer pair connectivity. Next, production well performances are evaluated through multiple diagnostic models. Finally, the impact of injectors on oil production rates are analyzed and injector efficiencies are determined to establish a more efficient waterflooding strategy. The proposed methodology has been applied to a reservoir with around 50 producers and 7 injectors. The interwell connectivity between pairs have been identified and ranked. Using data analytics techniques on multiple surveillance data sets, the analysis of the waterflood is achieved more swiftly and accurately. It was observed that for this specific case, the most informative signals that help determine connectivity are the water cut, and water production rate. The identified injector-producer connections obtained from these models were further verified and compared well with additional available surveillance data on tracers for this reservoir. Understanding these leads to devising optimum waterflooding strategies such as diverting more injection water to the more efficient injectors and less injection water to the inefficient injectors. A novel multi-signal analysis using ML techniques is proposed that combines multiple data being collected as part of surveillance. The presented approach can be extended to similar waterfloods to help with optimizing the waterflooding strategy. This new approach helps with current digitization strategies in oil companies that seek to obtain faster and consistent solutions to accelerate decision making and as an alternative to cases especially where reservoir model is poorly defined.
Water flooding is an established method of secondary recovery to increase oil production in conventional reservoirs. Analytical models such as capacitance resistance models (CRM) have been used to understand the connectivity between injectors and producers to drive optimization. However, these methods are not applicable to waterflood fields at the initial stage of life with limited data (less than 2 years of injection history). In this work, a novel approach is presented that combines analytics and machine learning to process data and hence quantify connectivity for optimization strategies. A combination of statistical (cross-correlation, mutual information) and machine learning (linear regression, random forest) methods are used to understand the relationship between measured injection and production data from wells. This workflow is first validated using synthetic simulation data with known reservoir heterogeneities as well as known connectivity between wells. Each of the four methods is validated by comparing the result with the CRM results, and it was found that each method provides specific insights and has its associated limitations making it necessary to combine these results for a successful interpretation of connectivity. The proposed workflow is applied to a complex offshore Caspian Sea field with 49 production wells and 8 injection wells. It was observed that implementing the diffusivity filter in the models while being computationally expensive, offers additional insights into the transmissibility between injector producer pairs. The machine learning approach addresses injection time delay through feature engineering, and applying a diffusive filter determines effective injection rates as a function of dissipation through the reservoir. Hence, the combined interpretation of connectivity from the different methods resulted in a better understanding of the field. The presented approach can be extended to similar waterflood systems helping companies realize the benefits of digitization, in not just accessing data, but also using data through such novel workflows that can help evaluate and continuously optimize injection processes.
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