In recent years, improved/enhanced oil recovery by tuning the ionic composition of injection water has attracted the attention of the petroleum industry, and currently deemed as new emerging research trend. In view of research results for the last four years, we demonstrated in previous reports (SPE 137634; SPEREE Journal, vol. 14(5), pp. 578-593; SPE 143550, SPE 141082, SPE 154076; SPE 154077) that substantial oil recovery beyond conventional waterflooding from carbonates can be achieved by optimizing the salinity and ionic composition of field injection brine. Similar potential has been confirmed also in the secondary recovery mode. For recovery mechanisms, research confirmed that the driving mechanism is wettability alteration of carbonate rock surface and can be attributed to surface charges alteration, and microscopic dissolution of anhydrite. In this paper, we present the results of two field trials conducted in a carbonate reservoir to demonstrate the SmartWater Flood potential. Both field trials confirmed that in-house research results can be replicated at field scale. Injection of SmartWater revealed a reduction of ~7 saturation units in the residual oil beyond conventional seawater. Considering these field trials are the first-ever applications in carbonate reservoirs, they further provided another confirmation that SmartWater Flood has significant potential to be a new recovery method targeting carbonate reservoirs. A special type of single-well chemical tracer was used in these trials to measure the residual oil in the vicinity of the well following the injection of each water type. During all stages of field trials, careful QA/QC program was put in place to monitor variation in ionic composition in all injected or produced fluids and further insure optimum ionic composition of SmartWater slugs. Several field trials are planned to optimize the current process leading to a multi-well Demonstration Pilot to determine the impact on ultimate recovery and reserves.

Application of the Surrogate Reservoir Model (SRM) to an onshore green field in Saudi Arabia is the subject of this paper. SRM is a recently introduced technology that is used to tap into the unrealized potential of the reservoir simulation models. High computational cost and long processing time of reservoir simulation models limit our ability to perform comprehensive sensitivity analysis, quantify uncertainties and risks associated with the geologic and operational parameters or to evaluate a large set of scenarios for development of green fields. SRM accurately replicates the results of a numerical simulation model with very low computational cost and low turnaround period and allows for extended study of reservoir behavior and potentials. SRM represents the application of artificial intelligence and data mining to reservoir simulation and modeling.In this paper, development and the results of the SRM for an onshore green field in Saudi Arabia is presented. A reservoir simulation model has been developed for this green field using Saudi Aramco's in-house POWERS™ simulator. The geological model that serves as the foundation of the simulation model is developed using an analogy that incorporates limited measured data augmented with information from similar fields producing from the same formations. The reservoir simulation model consists of 1.4 million active grid blocks, including 40 vertical production wells and 22 vertical water injection wells.Steps involved in developing the SRM are identifying the number of runs that are required for the development of the SRM, making the runs, extracting static and dynamic data from the simulation runs to develop the necessary spatio-temporal dataset, identifying the key performance indicators (KPIs) that rank the influence of different reservoir characteristics on the oil and gas production in the field, training and matching the results of the simulation model, and finally validating the performance of the SRM using a blind simulation run.SRM for this reservoir is then used to perform sensitivity analysis as well as quantification of uncertainties associated with the geological model. These analyses that require thousands of simulation runs were performed using the SRM in minutes.

Well-based Surrogate Reservoir Model (SRM) may be classified as a new technology for building proxy models that represent large, complex numerical reservoir simulation models. The well-based SRM has several advantages over traditional proxy models, such as response surfaces or reduced models. These advantages include (1) to develop an SRM one does not need to approximate the existing simulation model, (2) the number of simulation runs required for the development of an SRM is at least an order of magnitude less than traditional proxy models, and (3) above and beyond representing the pressure and production profiles at each well individually, SRM can replicate, with high accuracy, the pressure and saturation changes at each grid block.Well-based SRM is based on the pattern recognition capabilities of artificial intelligence and data mining (AI&DM) that is also referred to as predictive analytics. During the development process the SRM is trained to learn the principles of fluid flow through porous media as applied to the complexities of the reservoir being modeled. The numerical reservoir simulation model is used for two purposes: (1) to teach the SRM the physics of fluid flow through porous media as applied to the specific reservoir that is being modeled, and (2) to teach the SRM the complexities of the heterogeneous reservoir represented by the geological model and its impact on the fluid production and pressure changes in the reservoir.Application of well-based SRM to two offshore fields in Saudi Arabia is demonstrated. The simulation model of these fields includes millions of grid blocks and tens of producing and injection wells. There are four producing layers in these assets that are contributing to production. In this paper we provide the details that is involved in development of the SRM and show the result of matching the production from the all the wells. We also present the validation of the SRM through matching the results of blind simulation runs.The steps in the development of the SRM includes design of the required simulation runs (usually less than 20 simulation runs are sufficient), identifying the key performance indicators that control the pressure and production in the model, identification of input parameters for the SRM, training and calibration of the SRM and finally validation of the SRM using blind simulation runs.

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Surfactant/polymer (SP) flooding is of particular interest in recent years due to its synergetic effects of interfacial tension reduction and mobility control with minimal side effects. This work focuses on constructing an SP simulation model using laboratory data and validating it by matching coreflooding results. A series of SP coreflooding experiments were performed in carbonate cores under reservoir conditions. Chemical injection was implemented in tertiary mode with varying slug sizes and concentrations. The coreflooding results show significant oil recovery potential for SP formulations under the conditions investigated. The base SP flood resulted in 23.4% incremental recovery after waterflooding with the polymer and surfactant contributions being about the same. The results also demonstrate the effects of surfactant slug-size and concentration on the recovery performance. Using UTCHEM the input parameters, necessary to predict incremental recoveries, were investigated. A general SP simulation model was initiated, in which: polymer viscosity dependence on concentration and salinity were established in the laboratory; surfactant phase behavior parameters were generated from test-tube results; and oil desaturation was based on additional coreflooding. After matching water and polymer flooding results, the surfactant simulation model was tuned through history matching the performance of a series of SP corefloods. A subsequent sensitivity analysis establishes the confidence level of the input parameters. The sensitivity analysis also highlights the significance of IFT reduction. Finally, we numerically investigated the optimum chemical formulation. Optimization runs were performed under a fixed chemical consumption condition. The results support the optimality of previously selected slug sizes while suggesting the potential benefit of increasing the polymer: surfactant concentration ratio. In summary, this work provides a predictive SP simulation model that can be used to upscale laboratory results to field-scale predictions.