This paper describes an efficient, accurate, and timesaving approach for setting well allowable using advanced and automated workflows in a digital oil field with more than 300 producing and injecting strings from multi-layered reservoirs having varied reservoir characteristics. This paper provides an insight on the usage of ADNOC shareholders guidelines, well characteristics, surface facility constraints, and integrated asset models to compute the well allowable rate. An integrated asset operations model (IAOM) within a digital framework provides an automation of engineering approach where shareholder/reservoir management guidelines, in conjunction with a calibrated well and network models, are used to improve efficiency and accuracy of setting wells allowable. This process incorporates the interaction among various components, including wellbore dynamics (Inflow and outflow performance), surface network backpressure effect, and complex system constraints. "System Efficiency and Well Availability" factors as well as predicted well parameters such as GOR and watercut. This advance workflow computes the rate that can be delivered from each well corresponding to each guideline and constraint, thereby providing key inputs to various business objective scenarios for production efficiency improvement. This automated "Setting Well Allowable" workflow, using an IAOM solution in a digital framework, has enabled the asset to identify true potential of wells and overcoming potential challenges of computational time saving while identifying opportunities. This automated validation workflows ensured usage of updated and validated well models, allowing effective use of the well test information and real time data for further analysis and sensitivities. The use of the automated workflow has reduced the time to compute the well allowable rates and well technical rates by more than 50%. This workflow prevented engineers from performing tedious manual calculations on a well-by-well basis, therefore engineers focus on engineering and analytical problems rather than collecting data. Additionally, this robust engineering approach provides users with key information associated with a well's performance under various guideline index such as potential rates, well technical rate, minimum backpressure rate, rate to maintain drawdown/ minimum bottom hole pressure limit to ensure a homogenous reservoir withdraw to avoid pressure sink areas. This work process also highlights the wells with increased watercut (WC) and gas oil ratio (GOR), thus providing crucial information for deteriorating well performance. A short-term forecasting with diagnostic curve fitting and trend analysis enabled users to validate deliverability of allowable rates in a calibrated network model scenario, thereby incorporating potential surface constraints and facility bottlenecks. The robustness of advanced and automated setting of well allowable workflow enables the operator to establish well performance with a solid engineering analysis base, and thereby unlocks key opportunities for saving cost, computational time and assuring short-term production mandate deliverables. This approach supports standardization of the work process across the whole organization.
Miscible HC-WAG injection is a globally implemented EOR method and seems robust in so many cases. Some of the largest HC-WAG projects are found in major carbonate oil reservoirs in the Middle-East, with miscibility being the first measure to expect the success of a HC-WAG injection. Yet, several miscible injection projects reported disappointing outcomes and challenging implementation that reduces the economic attractiveness of the miscible processes. To date, there are still some arguments on the interpretation of laboratory and field data and predictive modeling. For a miscible flood, to be an efficient process for a given reservoir, several conditions must be satisfied; given that the incremental oil recovery is largely dependent on reservoir properties and fluid characteristic. Experiences gained from a miscible rich HC-WAG project in Abu Dhabi, implemented since 2006, indicate that an incremental recovery of 10% of the original oil in place can be achieved, compared to water flooding. However, experiences also show that several complexities are being faced, including but not limited to, issues of water injectivity in the mixed wettability nature of the reservoir, achieving miscibility conditions full field, maintaining VRR and corresponding flow behavior, suitability of monitoring strategy, UTC optimization efforts by gas curtailment and most importantly challenges of modeling the miscibility behavior across the reservoir. A number of mitigation plans and actions are put in place to chase the positive impacts of enhanced oil recovery by HC-WAG injection. If gas injection is controlled for gravity and dissolution along with proper understanding on the limitations of WAG, then miscible flood will lead to excellent results in the field. The low frequency of certain reservoir monitoring activities, hence less available data for assessment and modeling, can severely bound the benefits of HC-WAG and make it more difficult to justify the injection of gas, particularly in those days when domestic gas market arises. This work aims to discuss the lessons learned from the ongoing development of HC-WAG and attempts to comprehend miscible flood assessment methods.
Reservoir simulations constitute a cornerstone to predict the flow of fluids through porous media. Various numerical models called simulators are developed to simulate the performance of hydrocarbon reservoirs. These models are used in field development since production forecasts are necessary to make investment decisions. Nowadays, numerical simulators are widely used by reservoir engineers. In recent times, machine learning applications have garnered the interest of the oil and gas industry due their unorthodox approach to creating complex models. The more historical data that can be provided in the training phase of the computation, the more accurate the predictions utilizing less time and computational power required by the alternative. The alternative, reservoir simulation applications, require more robust hardware for processing large amounts of data in sophisticated ways. This will require several iterations and may require long computation to validate the model. Running time for a simulation is dependent on software, which can be dependent on hardware. This creates a matrix of time and resources needed to complete simple to complicated simulations. A few studies have proposed the use of the Lanczos decomposition method in reservoir simulation studies. The attractiveness of this method appears to be the avoidance of time stepping in simulation and allows the computation of reservoir pressures at any given time directly. In this study, two new simulators were developed using Lanczos Decomposition Method (LDM) and Conventional Implicit Time-Stepping Method (ITSM). The study focuses on 2-D flow for slightly compressible fluid of constant viscosity with multiple wells. Derivation of the model equations was performed using the continuity equation for both methods through the use of MATLAB. The simulators were written using the MATLAB programming language. The simulators developed in this study are capable of assigning uniform and non-uniform gridblock distribution; porosity and permeability distributions, as well as developing various production and injection scenarios for single or multiple wells depending on different areas of application. Validity and accuracy of the 2D flow simulator were examined by comparing simulation results with that obtained from the commercial software called ECRIN. The results of the simulator were almost identical with the results obtained from the commercial software. During the model runs, the CPU time of the two simulators were compared. A special case was also studied for a single well with variable rate history using both ITSM and LDM written with FORTRAN. To date, in petroleum engineering literature, there is no work published that compares the performances (in terms of computational aspects as well as CPU times) of the Lanczos method and the conventional implicit-time stepping method.
Automation and data-driven models have been proven to yield commercial success in several oil fields worldwide with reported technical advantages related to improved reservoir management. This paper demonstrates the implementation of an integrated workflow to enhance CO2 injection project performance in a giant onshore smart oil field in Abu Dhabi. Since commissioning, proactive evaluation of the reservoir management strategy is enabled via smart-exception-based surveillance routines that facilitate reservoir/pattern/well performance review and supporting the decision making process. Prolonging the production sustainability of each well is a key pillar of this work, which has been made more quantifiable using live-tracking of the produced CO2 content and corrosion indicators. The intensive computing technical tasks and data aggregation from different sources; such as well testing and real time production/injection measurements; are integrated in an automatic workflow in a single platform. Accordingly, real-time visualizations and dashboards are also generated automatically; to orchestrate information, models and multidisciplinary knowledge in a systematic and efficient manner; allowing engineers to focus on problematic wells and giving attention to opportunity generation in a timely manner. Complemented with numerical techniques and other decision support tools, the intelligent system data-driven model assist to obtain a reliable short-term forecast in a shorter time and help making quick decisions on day-to-day operational optimization aspects. These dashboards have allowed measuring the true well/pattern performance towards operational objectives and production targets. A complete set of KPI's has helped to identify well health-status, potential risks and thus mitigate them for short/long term recovery to obtain an optimum reservoir energy balance in daily bases. In case of unexpected well performance behaviors, the dashboards have provided data insights on the root causes of different well issues and thus remedial actions were proposed accordingly. Maintaining CO2 miscibility is also ensured by having the right pressure support around producers, taking proactive actions from continues evaluation of producer-injector connectivity/interdependency, improving injection/production schedule, validating/tuning streamline model based on surveillance insights, avoiding CO2 recycling, optimizing data acquisition plan with potential cost saving while taking preventive measures to minimize well/facility corrosion impact. In this work, best reservoir management practices have been implemented to create a value of 12% incremental oil recovery from the field. The applied methodology uses an integrated automation and data-driven modeling approach to tackle CO2 injection project management challenges in real-time.
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