This work summarizes the prospect of EOR and sequestration using CO2 flooding from an Indian mature oil field in Assam through laboratory study, reservoir static modeling, dynamic simulation, pilot design, and techno-economic sensitivity studies. The geomodel was established by incorporating of contour maps, well positions and coordinates, well data and well logs, perforation depths and distribution of petrophysical properties as well as fluid properties. It was confirmed through PVT laboratory studies that CO2 injection can achieve the miscibility under reservoir conditions. The coreflooding test showed the significant incremental oil recovery by continuous CO2 injection and the residual oil saturation after miscible CO2 injection reached ~0.13PV. A fine scale geological model was built for entire reservoir and dynamic simulation work was performed on the geological model without upscaling. The history match of 51-year field production and pressure data in the whole reservoir was completed in a commercial simulator, and various development scenarios were investigated. Based on the results from CO2 EOR simulation study, we identified a pilot pattern area of ~ 60 acres with one injector and four producers. The CO2 was injected into reservoir at 150 metric ton per day for 5 years and cumulative injection volume is 15.4 BCF. Then the well is switched back to water injection afterward. Around 1 million STB incremental oil recovery was obtained in about 10 years, which corresponds to 11% of original oil in place in the flooded area. The CO2 utilization ratio is approximately 6 MCF/BBL. It is expected that CO2 flooding yields a pre-tax net cash flow of US dollars of 9.4 MM. CO2-EOR and storage in this mature field has a great techno-economic prospect. The investigation of CCUS opportunity and the substantial advancement in CO2 flood pilot design project have created an excitement in Indian Oil& Gas industry since the CCUS can significantly improve the domestic oil production from mature oilfields, and also reduce the carbon footprint in India. The volume of anthropogenic CO2 injection and storage in the reservoirs presents the great social and economic benefits for CCUS in India.

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Offset well analysis is the process of investigating and integrating historical drilling performance from neighboring wells into prospect well design. Traditional offset analysis is a time and resource intensive process that requires a lot of manual input and analysis to make various design decisions. Our proposed workflow automates much of this analysis, while allowing the user to customize designs based on operating metrics resulting in a quicker and more comprehensive offset well analysis that provides the user with intelligent offset recommendations and a base design for the prospect well. Historical data is queried from a structured engineering and operation database comprising of data ranging from subsurface geology, drilling to production and end-of-well. We gather this historical data from nearby wells and learn from those in order to produce a first pass design for the prospect well to begin with. With this data, our proposed workflow implements algorithms to identify representative offset wells with similar geology, trajectory and other characteristics. A similarity analysis based on various geological sequences across the wells is conducted by a deep neural network algorithm, which is trained to analyze sequential patterns within. Trajectory similarity is performed using well surveys (dogleg severity, inclination, and azimuth). A recurrent neural network is employed to learn the well survey patterns and classify wells with similar trajectories. Finally, drilling performance metrics are used to rank the offsets and aid selection of the best offset design to be used as a base template for the prospect well. The proposed workflow significantly reduces analysis time; the software analysis time is less than five minutes. The user can almost instantaneously query the database and obtain wells that are similar in terms of trajectory and geology, and rank those wells on certain pre-defined metrics. The engineer is also able to view key events and hazards history for the offset wells. This provides the engineer with a compilation of hazards and risks by depth and cause, allowing the engineer to focus on mitigating risks and designing better and safer wells. The engineer can accept the top ranked offset and automatically select casing design based on default metrics of cost, time, and NPT, or can implement other metrics and create a composite casing design from different hole-sections from different offsets.

Optimal injector selection is a key oilfield development endeavor that can be computationally costly. Methods proposed in the literature to reduce the number of function evaluations are often designed for pattern level analysis and do not scale easily to full field analysis. These methods are rarely applied to both water and miscible gas floods with carbon storage objectives; reservoir management decision making under geological uncertainty is also relatively underexplored. In this work, several innovations are proposed to efficiently determine the optimal injector location under geological uncertainty. A geomodel ensemble is prepared in order to capture the range of geological uncertainty. In these models, the reservoir is divided into multiple well regions that are delineated through spatial clustering. Streamline simulation results are used to train a meta-learner proxy. A posterior sampling algorithm evaluates injector locations across multiple geological realizations. The proposed methodology was applied to a producing field in Asia. The proxy predicted optimal injector locations for water and CO2 EOR and storage floods within several seconds (94–98% R2 scores). Blind tests with geomodels not used in training yielded accuracies greater than 90% (R2 scores). Posterior sampling selected optimal injection locations within minutes compared to hours using numerical simulation. This methodology enabled the rapid evaluation of injector well location for a variety of flood projects. This will aid reservoir managers to rapidly make field development decisions for field scale injection and storage projects under geological uncertainty.