Search

Waterfloods are amongst the most widely implemented methods for oil field development. Despite their vast implementation, operational bottlenecks such as lack of surveillance and optimization tools to guide fast paced decisions render most of these sub-optimal. This paper presents a novel machine-learning, reduced-physics approach to optimize an exceptionally complex off-shore waterflood in the Gulf of Suez. Leveraging a hybrid data-driven and physics approach, the water flooding scheme in Nezzezat reservoir was optimized to improve reservoir voidage replacement, increase oil production, and reduce water production by identifying potential in wells. As a by-product of the study, a better understanding of the complex fault system was also achieved. Including the geological understanding and its uncertainty is one of the key elements that must be preserved. All geological attributes, along with production rates are used to solve for pressure and inter-well communication. This is later supplemented by machine-learning algorithm to solve for the fractional flow of inter-well connections. Combining the inter-well connectivity and fractional flow, an optimization was performed to reach the best possible conditions for oil gains and water-cut reduction. A global optimization is possible thanks to the low computational demand of this approach, as thousands to millions of realizations must be run to reach the best solution while satisfying all constraints. This is all done in a fraction of the time it takes to run a traditional reservoir simulation. For the present case, the paper will present the underlying physics and data-driven algorithms, along with the blind tests performed to validate the results. In addition to the method's inner workings, the paper will focus more on the results to guide operational decisions. This is inclusive of all the complex constraints of an offshore field, as well as the best reservoir management practices, when reaching optimal production and injection rates for each well. An increase in production was achieved with some reduction in water-cut, while honoring well and platform level limitations. While these represent the gains for a particular month, optimization scenarios can be run weekly or monthly to capture the dynamic nature of the problem and any operational limitations that might arise. The ability to update the models and run optimization scenarios effortlessly allows pro-active operational decisions to maximize the value of the asset. The approach followed in this paper solves for the critical physics of the problem and supplements the remaining with machine learning algorithms. This novel and extremely practical approach facilitate the decision making to operate the field optimally.

This paper presents the comprehensive analysis and optimization study of the gas lift system of 44 gas lifted wells in QQ Field in the Gulf of Suez. With the least amount of intervention, the optimized operational plan would yield an additional 7 to 10% oil gain over the current production rate, an improvement of gas utilization and operational efficiency, and operating cost reduction. This paper presents an Augmented AI approach, which is an efficient, repeatable and automated workflow to diagnose and optimize gas lift operations, reducing the time required for this process from weeks to hours. This study evaluates historical performance, identification of suboptimal performers using heuristic diagnostics, and subsequent optimization using well models coupled with advanced optimization algorithms. The presented workflow implements a top-down approach beginning with field-level based analytics, then further delves into detailed engineering aspects of well production and individual gas lift operations. It builds from artificial intelligence and engineering-based workflows that compute smart metrics based on the historical well production and performance of gas lift systems. Heuristics and expert-based rules are also considered to perform a fast and smart diagnostics process. These analytics and diagnostics stages help build key metrics to evaluate well performance and monitor gas lift operations from both executive and asset management levels. The study results identified suboptimal wells with inefficient gas lift performance that require detailed evaluation for improvement from both design and operational perspectives. During the optimization phase, reliable well model-based analysis coupled with physical constraints are used to provide remedial actions for the identified wells. The workflow allows automated well model construction and calibration, coupled with an unsupervised learning algorithm to detect and eliminate unreliable datasets. Afterward, specific well-level analysis is performed including gas lift performance curve while honoring existing well configuration, hypervolume-based multi-objective optimization and sensitivity analysis on key parameters for best gas lift performance.

The QQ Field in the Gulf of Suez is a mature, geologically complex with multiple stacked, faulted reservoirs, with commingled production between different reservoirs. This paper illustrates the power of an automated tool to perform systematic, rapid, and detailed assessment of the reservoir performance, identify the key recovery obstacles and prepare remedial plans to enable the reservoir to produce to its full potential. The well and reservoir data were processed to compute a series of metrics and key performance indicators at various levels (well, layer, reservoir, well groups, etc.). The tool has several automated modules to facilitate rapid, metric-driven reservoir assurance and management. These modules include: (i) well production/injection allocation, (ii) wells decline curve analysis including event-detection, (iii) pressure and voidage analysis, and (iv) Contact analysis. Using performance analytics, the study quickly identified ways to improve the health of the reservoir and maximize its value. The QQ Field predominantly produces from two formations: Nubia and Nezzazat. Furthermore, there are multiple sub-layers in each formation. Reliable flow unit allocation is critical to gauge contribution of each layer, identify the undrained areas of the reservoir, and locate future development opportunities. The flow unit allocation module incorporates all available data such as PLT/ILT data, completion history, permeability of each flow unit at well level, relative pressures, and water influx model. Based on the allocated production, the current recovery factors in Nubia and Nezzazat are approximately 60% and 20% respectively. Analysis of RFT data reveals good vertical communication across Nubia. However, in some areas there is clear pressure discontinuity across layers. The reservoir pressure has dropped below the bubble point in both formations. As a result, water injection was initiated. The pressure in all parts of Nubia was restored above bubble point. Aquifer influx is sufficient to support the current withdrawal rates and further water injection is unnecessary. However, Nezzazat has a significantly higher reservoir complexity and therefore, shows a large variation in pressure behavior. It needs water injection to maintain the reservoir pressure above the bubble point in all parts of the reservoir. Based on the flow-unit allocation, the voidage replacement ratio (VRR) was calculated for each area and each layer. Even though the overall VRR in the waterflooded areas is above one, the distribution of the injected water is uneven. Redistributing injected water and ensuring that all the areas and all the layers are adequately supported will help to maximize recovery. The prolonged dip in oil price demands extreme efficiency. Sound reservoir management must not require unreasonable time or manpower. The rapid, automated analysis enables quick identification of the key areas for improvement in reservoir management practices and maximize the value of the asset.

Systematic and comprehensive analysis of gas lift operations must include historical performance evaluation and heuristic diagnostics for identification of suboptimal performers. Reliable nodal analysis models are necessary to optimize the gas lift systems. Though some degree of automation is currently possible, this process still takes substantial effort and time. The work presented here is a streamlined, repeatable, and automated workflow to diagnose and optimize gas lift operation through analytics, reliable wellbore modeling and calibration that reduces the time from days to hours. The workflow is also very easily repeatable, which makes it very efficient to update when new data is available. The workflow combines the emerging data analytics efforts in the oil and gas industry along with traditional gas lift optimization guidelines to identify artificial lift-related key recovery obstacles and corresponding development plans. This novel combined approach proves the effectiveness and necessity of augmented artificial intelligence, and the case study of field analysis and execution further endorse the impact of such optimization workflow for gas lift assessment and operations. It builds from data-driven and engineering-based workflows that compute smart metrics and dashboards based on the historical well production and the gas lift systems performance. Several KPIs and metrics are utilized, some of which are industry standard while others are intuitively built exclusively for this workflow. In this paper, we present the results and challenges in applying this workflow to an offshore Middle East oilfield, with over 50 gas lift wells. As always with field data, several aspects of the data had to be addressed. Accuracy and relevancy of data was questionable. We developed several workflows to address the missing/incorrect data to ensure the robustness and credibility of the results from the workflow. Due to the practical implication of the analysis, stringent quality control and error margins were maintained throughout the analysis. No nodal analysis models were available, consequently models were built and calibrated as a part of this effort. Overall field and facility constraints were also incorporated to ensure results were achievable in the field. The optimized operational plan would yield an additional 15 to 20% gains over current production rate. Further benefits by upgrading the gas lift design are also quantified using the proposed workflow. The time required to construct, calibrate, and optimize around 50 wells was less than two weeks.

Maintaining and sustaining reservoir performance and health is a priority that requires ongoing assurance activities that will maximize recovery up to industry best practices. An automated and integrated reservoir performance tool has been developed to provide high-level assurances at both the reservoir and asset level. The Reservoir Management Performance Index (RMPI) is a set of indices that can identify key performance issues involving several aspects of the reservoir'sdevelopment and operational plans. This tool identifies mitigating measures that require action, assures production sustainability, promotes a reservoir-focused organization, and standardizes the reservoir performance evaluation in an organization. RMPIprovides a high-level overview and a platform for all management and operation levels, where observing the same set of results can initiate collective decisions that improve reservoir management. Such a system was developed for a company to monitor and measure the performance against expected standards and forecasts for the large number of reservoirs in its portfolio. This tool measures multiple aspects of reservoir management grouped into four major categories: Energy Management, Forcasting Relaibility, Reserves Management and Operations. The tool is tailored to account for various aspects such as: stage of maturity of the reservoir, primary or secondary depletion stage, etc. Each category consists of multiple individual metrics that combine actual field data with targets/forecasts and use an algorithm to calculate a score. These scores are weighted and aggregated for an overall score in each category and an overall score for the asset/reservoir itself. Several aspects accounted for in the metrics include (but not limited to): pressure management, voidage replacement, water and gas management, production and injection performance, reserves promotion and replacement, current RF, EURF, drilling and workover efficiency, Well-Up time, etc.

Summary The work presented in this paper is part of a larger research project which is aimed at finding solutions to problems associated with liquid loading, erosion at pipe bends caused by sand particles, and phase separation. The work uses computational fluid dynamics (CFD) to design solutions that can reduce or eliminate the aforementioned problems. Here, the results from CFD simulations of two-phase air and water flows are critically analyzed through comparison with the results from experiments carried out by Falcone et al. (2003) using the ANUMET concept.† The entire experimental setup is modeled within the CFD simulation and flow rates for water and air are taken from the data used for the experiments. Important variables such as pressure drop and fluid film thickness, which were monitored closely during the experiments, are obtained from the CFD simulations and compared with the experimental results. The results presented in this paper provide insights into the physics of two-phase swirl flows, identifying areas of research that still need to be addressed.