Leveraging Data Analytics with Numerical Modeling for Optimizing Oil Field Development and Management

Using commercial numerical reservoir simulators to build a full field reservoir model and simultaneously history match multiple dynamic variables for a highly complex, offshore mature field in Malaysia, had proven to be challenging, manpower intensive, highly expensive, and not very successful. This field includes almost two hundred wells that have been completed in more than 60 different, non-continuous reservoir layers. The field has been producing oil, gas and water for decades. The objective of this article is to demonstrate how Artificial Intelligence (AI) and Machine Learning is used to build a purely data-driven reservoir simulation model that successfully history match all the dynamic variables for all the wells in this field and subsequently used for production forecast. The model has been validated in space and time. The AI and Machine Learning technology that was used to build the dynamic reservoir simulation and modeling is called spatio-temporal learning. Spatio-temporal learning is a machine-learning algorithm specifically developed for data-driven modeling of the physics of fluid flow through porous media. Spatio-temporal learning is used in the context of Deconvolutional Neural Networks. In this article Spatio-temporal Learning and Deconvolutional Neural Networks will be explained. This new technology is the result of more than 20 years of research and development in the application of AI and Machine Learning in reservoir modeling. This technology develops a coupled reservoir and wellbore model that for this particular oil & gas field in Malaysia uses choke setting, well-head pressure and well-head temperature as input and simultaneously history matches Oil production, GOR, WC, reservoir pressure, and water saturation for more than a hundred wells through a completely automated process. Once the data-driven reservoir model is developed and history matched, it is blind validated in space and time in order to establish a reliable and robust reservoir model to be used for decision making purposes and opportunity generation to maximise the field value. The concepts and the methodology of history match of multiple wells, individual offshore platforms, and the entire field will be presented in this article along with the results of blind validation and production forecasting. Results of using this model to perform uncertainty quantification will also be presented. A case study of a highly complex mature field with large number of wells and years of production has been used to be studied and simulated by this data-driven approach. Time, efforts, and resources required for the development of the dynamic reservoir simulation models using AI and Machine Learning is considerably less than time and resources required using the commercial numerical simulators. It is validated that the TDM developed model can make very reasonable prediction of field behavior and production from the existing wells based on modification of operational constraints and can be a prudent complementary tool to conventional numerical simulators for such complex assets.

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

This paper outlines critical reservoir management issues and technical areas for effectively managing water and gas floods. A tie between reservoir modelling and performance prediction and decision analysis is illustrated. Case studies are discussed to show the contributions of new and integrated technologies to effective reservoir management. In summary, the companies with a technological leadership position in this area are beginning to harvest the fruits of their actions. Introduction Integrated reservoir management and technology are being increasing applied to maximize economic recovery of oil and gas. The need to enhance recovery from the vast amount of remaining oil- and gas-in-place from existing reservoirs and to maximize recovery from newly discovered fields require the use of integrated technologies and better reservoir management practices(1), Historically, some form of reservoir management has been practiced when a major expenditure is planned, such as a new field development or waterflood installation. The reservoir management practices in these instances most often were not integrated, i.e., different disciplines did their own work separately. During the last five years, however, greater emphasis has been placed on synergism between various functional disciplines, and many companies practicing this are benefiting from their synergistic activities(2). Manyleading-edge technological advances have now been made in geophysics, geology, petrophysics, and reservoir and operations engineering. Mainframe super computers, more powerful personal computers, and workstations are providing ever increasing computing power. Integrated, life-cycle, database-management systems are also being developed and deployed. Integrated, interactive 3D reservoir modelling and simulation assist in describing reservoir geometry and property distribution. In addition, integrated 3D graphics workstations and parallel processors provide the tools to achieve reservoir characterization and modelling efficiently by reviewing the simulation results with other reservoir descriptions and static data and by changing the model, if necessary. Thus, it is now possible for geoscientists and engineers to use new developments in geologic modelling and geostatistical techniques to build a 3D model from static data and edit the model while history matching dynamic data. Combining geostatistical reservoir characterization with high resolution 3D seismic interpretation provides a new dimension in accurately modelling the reservoir. On the other hand, new technologies such as hybrid simulation and scale up provide alternatives to a large-scale computer simulation, yet retaining the geologic heterogeneities in the model. This paper first describes major elements of a successful reservoir management program, and then documents case studies illustrating sound reservoir management and decision analysis practices pertaining to water- and gasflood projects. Elements of Successful Reservoir Management (1) The major elements of a successful reservoir management program are identified below. Data Collection and Management Data collection and management are very important to project success. A clear understanding of the purpose and application of the data is needed, i.e., define why the information is needed and how it is to be used. A cost-benefit analysis of the data, that is, the cost of data collection and management and the benefits to be derived, is mandatory.

There have been published concerns that the log interpretation programs in the world's freshwater (low-contrast, low-resistivity) environments have not been providing accurate water saturation estimates, and that estimation of residual oil saturations is erroneous, as previous coring and core analysis procedures were not designed to maintain reservoir rock wettability. One of the most serious problems (and uncertainty) in field development plan optimisation and reservoir management in these freshwater environments, have always been high initial water saturation (with wide range of saturation values), inconsistent residual oil saturation results and lack of quality reservoir dynamic, mechanical and petrophysical properties data. To address these data uncertainty issues, a special drilling, core recovery and core analysis program was conducted on three key wells in the Malay Basin's Dulang Field, applying the latest technology, field-proven low-invasion core heads, bland water-base mud (WBM) and a low-toxicity, bland oil-base mud (LT-OBM). A minimum-flushing LT-OBM coring facilitated a more accurate determination of initial water saturation (Swi) required for recalibration of downhole log calculations and a more accurate estimation of reserves. A maximum-flushing WBM sponge-coring permitted an accurate determination of residual oil saturation (Sor). A sophisticated tracer technology was implemented using hexachloroethane tracer that provided accurate invasion profiles. The 210 meters of fresh, preserved cores recovered from the three wells, covering over 70% of the stacked reservoir system, were subjected to a comprehensive laboratory special core analysis program. The results obtained from the core analysis program indicate a considerable narrowing (reduction) of the Swi range, from 19–61% to 20–43%. Water saturation exponent n has been improved from 1.89 to 1.64, and cementation exponent, "m" from 1.72 to 1.77. The impact of these improved parameter definition on reserves has been quantified through the computation of hydrocarbon-pore-volume of the cored wells. The results have shown an increase of 28.9% in HCPV, which translates to higher reserves; and have shown better-defined capillary pressure and relative permeability curves. The Swi and Sor data have been used to calculate recovery factors and expected ultimate recovery, the results of which duplicate those of computer simulation studies. All these provide greater confidence in further development and reservoir management of the field. This paper will discuss only details of the saturation results, including the quality assurance and quality control programs which validate the core recovery, core analysis and core-log data integration. Introduction The success of defining an optimum field development plan and reservoir management strategy for any field is critically dependent upon our knowledge and understanding of the reservoir rock and fluid properties, heterogeneity and the overall internal geometry / architecture of the reservoir system. Thus, the only sound basis for optimum development planning is a comprehensive, relevant and cost-effective data acquisition program. However, there are special problems associated with formation evaluation of fresh-water environments such as the Malay Basin. The reservoir quality of the reservoir sands is quite variable and primarily controlled by texture (grain size, sorting, angularity, etc), mineralogy (clay types, abundance, morphology, and location, and cements), degree of bioturbation and diagenesis. The salinities of formation waters are generally low and vary from less than 5,000 to 30,000 ppm. P. 411^

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

Machine learning (ML) has been leveraged to tackle a diverse range of tasks in almost all branches of nuclear engineering. Many of the successes in ML applications can be attributed to the recent performance breakthroughs in deep learning and the growing availability of computational power, data, and easy-to-use ML libraries. However, these empirical successes have often outpaced our formal understanding of the ML algorithms. An important but underrated area is the uncertainty quantification (UQ) of ML. ML-based models are subject to approximation uncertainty when they are used to make predictions due to sources including but not limited to, data noise, data coverage, extrapolation, imperfect model architecture, and the stochastic training process. The goal of this paper is to clearly explain and illustrate the importance of the UQ of ML. We elucidate the differences in the basic concepts of UQ of physics-based models and data-driven ML models. Various sources of uncertainties in physical modeling and data-driven modeling are discussed, demonstrated, and compared. We also present and demonstrate a few techniques to quantify the ML prediction uncertainties, including Monte Carlo dropout, deep ensemble, Bayesian neural networks, Gaussian processes, and conformal prediction. Finally, we discuss the need for building a verification, validation, and UQ framework to establish ML credibility.

Data acquisition is a cornerstone of effective reservoir management, playing a critical role in understanding and optimizing the extraction of hydrocarbon resources. For reservoir management engineers, the process of acquiring data involves several types, each tailored to specific needs and conditions. These include production data, well logs, and seismic data, among others. This data is utilized to construct a resilient proxy model utilizing machine learning algorithms, leading to precise predictions of field water production. Moreover, these runs are completed in a matter of seconds rather than days, unlike numerical commercial simulators, aiding decision-making during critical times in reservoir management. In the context of managing water resources within a reservoir, water saturation surveys conducted in observation wells are invaluable. These surveys help delineate the movement of water across the field, particularly in layered reservoirs where water dynamics can be complex. By mapping the shape and flow of water, engineers can more effectively calculate displacement efficiency and tailor production strategies to maximize output and minimize water production. The traditional approach to conducting periodic saturation log surveys can be both time-consuming and costly, typically constrained by the limited budget allocated for such activities annually. To address these challenges, this work has utilized a machine learning approach to optimize survey efforts. In this context, a numerical reservoir model was considered for mimicking the measurements of the saturation by generating various scenarios using space-filling design of experiments, viz., Latin Hypercube Design (LHD). The gained data from the numerical simulations was used for developing the proxy model based on various machine learning approaches, including tree-based (CatBoost) and neural network schemes (MLP and CFNN). The obtained results demonstrated the robustness of the implemented proxy model for predicting water saturation profiles. Validating the machine learning (ML) model against synthetic data generated by a black oil a compositional numerical model demonstrates a robust and accurate prediction of water breakthrough and water production in the field. This underscores the robustness of the experimental design used to orchestrate the numerical runs and the ML models developed in this investigation. The study highlights that the complexity of the ML algorithm escalates for highly intricate and heterogeneous reservoirs to enhance the accuracy of water prediction in the field. All ML algorithms performed effectively, with CatBoost proving particularly adept at capturing reservoir heterogeneity and complexity. The accuracy of the ML models has been assessed across various conditions by altering the bottom hole pressure and their effect on the different production rate during ten years water injection Our work flow demonstrate the successful application of our developed proxy model, resulting in reliable predictions of water production of producer wells. Additionally, computational time has been reduced by more than 10-fold compared to reservoir simulation runs. This predictive power allows for strategic selection of wells for detailed logging, based on significant changes in water saturation, thereby potentially reducing survey costs by 30-50%. This innovative model represents a significant advance in reservoir management, offering a way to minimize time and financial expenditure while maximizing the effectiveness of saturation surveys.

Summary This paper presents the major steps of a new integrated Reservoir Management Plan (iRMP) for the Piltun-Astokhskoye (PA) oil and gas condensate field, located offshore in the Sea of Okhotsk, off the North-Eastern coast of Sakhalin Island, Russian Federation (RF). This recaps background information about the previous RMPs which were prepared separately for the Astokh and Piltun Areas, the main challenges, outcomes and perspectives. The new iRMP-PA 2019 elaborates multiple field development scenarios implying large capital expenditures. The main purpose of this is in establishing a sustainable Field Development Plan (FDP) with considering the acquired experience and maximising the hydrocarbon ultimate recovery and the profit both for the Russian State and for the Company. The delivery of the iRMP-PA 2019 project allowed the Company to actualise its portfolio of geological and recoverable reserves in the RF State Reserves Balance. The new FDP with an optimised drilling sequence, long-term forecasts, reservoir data acquisition and pilot production programme has been reviewed and endorsed by the RF Central Development Commission. This will help extend the field life through maturation of contingent resources and through optimising the reservoir management strategy for all the assets.

This paper expounds the value of integrated decision based planning in delivering field development plan (FDP) in a LEAN way. The basic philosophy of lean is waste minimization or elimination of non-value adding activities to improve efficiency, quality and lead-time. Integrated decision based planning is considered as the most pragmatic and efficient approach in integrated reservoir modelling process. Dynamic modeling is the most preferred tool assisting subsurface decisions making. Nevertheless, the data centric approach with support of scaled reservoir model has the advantage over the conventional full field physics-based models specially, in case of a complex reservoir. Embracing the basic lean principles with focused decision based reservoir modeling strategy can establish a new level of performance within the organization in delivering FDPs. The Saih Rawl oil field (SRS) in North Oman is a thin Lower Cretaceous carbonate reservoir with post diagenetic imprint. Post oil fill structural change has resulted in re-saturation and oil trapping due to local capillary imbibitions. The complexities resulting from the tilted water contact, hysteresis in oil mobility and Sor variation with depth, pose a huge challenge in dynamic simulation. In addition, drilling feasibility for horizontal infill wells was quite challenging due to subsurface collision issues and rig footprint interference with existing surface facilities. Integrated decision based planning, linked to the subsurface and surface decisions was adopted for framing the integrated reservoir modeling (IRM) strategy. The IRM strategy with Decision Based Models (DBM), including analytical and sector simulation models, were used to understand the sweep pattern, locate the remaining oil and rank the various water-flood patterns. Data analysis including normalized decline-curve-analysis (DCA) based conduit models and comprehensive field performance analysis using Spotfire (an integrated data visualization and analysis tool by TIBCO), was used to understand the key reservoir management risks and infill potential. Throughout the process, the basic philosophy of lean was adopted embracing several lean tools to improve productivity, quality and lead-time. Out of 12 subsurface feasible options studied, the proposed optimum option envisages an increase in the oil recovery factor by 9% by drilling an additional 92 infill wells in 22 patterns. The successful completion of frontend loading phase of SRS project has achieved reducing in the FDP study time to 19 months compared to an average of 36 months in the past and project implementation 4 years ahead of the original plan. Fast tracking of the project implementation was possible due to standardization of the equipment, maximum utilization of the existing infrastructure and constructive collaboration with the stakeholders. The key enablers for the successful delivering of the SRS FDP study were mainly the integrated decision based planning with data centric approach in reservoir modeling workflow and adoption of basic lean principles This approach emphasizes the importance of adopting lean tools in frontend delivery process. The decision based planning with reservoir models linked to the project decision can significantly improve the efficiency and quality of the FDP. The stakeholder alignment and strong collaboration with key stakeholders of the project can further reduce the lead-time of project execution. The decision based IRM planning used for this study sets a benchmark for future FDP studies. The Urban Plan study approach for this project has also become the standard for other LEAN FDPs.

A Field development plan has been completed for a deepwater field located in gulf of guinea in water depth of around 1100 - 1400m offshore. The field is a confined turbidite channel system with some incised canyons and meandering channel complexes. The stacked nature of some of the reservoirs lends itself to combinations of drainage points. This can be harnessed to - reduce the well count by accessing multiple sands in one wellbore, accelerate production, and improve ultimate recovery from better reservoir management and/or development of reserves which cannot bear the cost of a dedicated conventional well. Smart well technology has been deployed in the field development plan to facilitate the realizations of these advantages and hence make the project economics more robust. In applying the technology, an integrated approach has been adopted all through the reservoir modelling, well planning and completion design sessions to ensure feasibility from the onset. A multi-disciplinary team and tools were fully deployed to ensure maximum gain realized in the smart well technology application. Based on the work done so far, the following justifies the appropriateness of the technology for the field: Smart wells overall value is in reducing the well count and overall CAPEX whilst maintaining the UR – Well count reduction by ∼20%. Well and reservoir management (WRM) Facilitation – Additional 10% to be gained from WRM via smart field implementation of appropriate level of smartness. This paper discusses the integrated approach and the tools that facilitated the process.

SummaryFull-physics models in history matching (HM) and optimization can be computationally expensive because these problems usually require hundreds of simulations or more. In a previous study, a physics-based data-driven network model was implemented with a commercial simulator that served as a surrogate without the need to build a 3D geological model. In this paper, the network model is reconstructed to account for complex reservoir conditions of mature fields and successfully apply it to a diatomite reservoir in the San Joaquin Valley, California, for rapid HM and optimization.The reservoir is simplified into a network of 1D connections between well perforations. These connections are discretized into gridblocks, and the grid properties are calibrated to historical production data. Elevation change, saturation distribution, capillary pressure, and relative permeability are accounted for to best represent the mature field conditions. To simulate this physics-based network model through a commercial simulator, an equivalent Cartesian model is designed where rows correspond to the previously mentioned connections. Thereafter, the HM can be performed with the ensemble smoother with multiple data assimilation (ESMDA) algorithm under a sequential iterative process. A representative model after HM is then used for well control optimization.The network model methodology has been successfully applied to the waterflood optimization for a 56-well sector model of a diatomite reservoir in the San Joaquin Valley. HM results show that the network model matches with field level production history and gives reasonable matches for most of the wells, including pressure and volumetric data. The calibrated posterior ensemble of HM yields a satisfactory production prediction that is verified by the remaining historical data. For well control optimization, the P50 model is selected to maximize the net present value (NPV) in 5 years under provided well/field constraints. This confirms that the calibrated network model is accurate enough for production forecasts and optimization. The use of a commercial simulator in the network model provided flexibility to account for complex physics, such as elevation difference between wells, saturation nonequilibrium, and strong capillary pressure. Unlike the traditional big-loop workflow that relies on a detailed characterization of geological models, the proposed network model only requires production data and can be built and updated rapidly. The model also runs much faster (tens of seconds) than a full-physics model because of the use of much fewer gridblocks.To our knowledge, this is the first time this physics-based data-driven network model is applied with a commercial simulator on a field waterflood case. Unlike approaches developed with analytic solutions, the use of a commercial simulator makes it feasible to be further extended for complex processes (e.g., thermal or compositional flow). It serves as a useful surrogate model for both fast and reliable decision-making in reservoir management.

Full-physics models in history matching and optimization can be computationally expensive since these problems usually require hundreds of simulations or more. We have previously implemented a physics-based data-driven network model with a commercial simulator that serves as a surrogate without the need to build the 3-D geological model. In this paper, we reconstruct the network model to account for complex reservoir conditions of mature fields and successfully apply it to a diatomite reservoir in the San Joaquin Valley (SJV) for rapid history matching and optimization. The reservoir is simplified into a network of 1-D connections between well perforations. These connections are discretized into grid blocks and the grid properties are calibrated to historical production data. Elevation change, saturation distribution, capillary pressure, and relative permeability are accounted for to best represent the mature field conditions. To simulate this physics-based network model through a commercial simulator, an equivalent 2-D Cartesian model is designed where rows correspond to the above-mentioned connections. Thereafter, the history matching can be performed with the Ensemble Smoother with Multiple Data Assimilation (ESMDA) algorithm under a sequential iterative process. A representative model after history matching is then employed for well control optimization. The network model methodology has been successfully applied to the waterflood optimization for a 56-well sector model of a diatomite reservoir in the SJV. History matching result shows that the network model honors field-level production history and gives reasonable matches for most of the wells, including pressure and flow rate. The calibrated ensemble from the last iteration of history matching yields a satisfactory production prediction, which is verified by the remaining historical data. For well control optimization, we select the P50 model to maximize the Net Present Value (NPV) in 5 years under provided well/field constraints. This confirms that the calibrated network model is accurate enough for production forecasts and optimization. The use of a commercial simulator in the network model provided flexibility to account for complex physics, such as elevation difference between wells, saturation non-equilibrium, and strong capillary pressure. Unlike traditional big-loop workflow that relies on a detailed characterization of geological models, the proposed network model only requires production data and can be built and updated rapidly. The model also runs much faster (tens of seconds) than a full-physics model due to the employment of much fewer grid blocks. To our knowledge, this is the first time this physics-based data-driven network model is applied with a commercial simulator on a field waterflood case. Unlike approaches developed with analytic solutions, the use of commercial simulator makes it feasible to be further extended for complex processes, e.g., thermal or compositional flow. It serves as an useful surrogate model for both fast and reliable decision-making in reservoir management.

• Enhanced SSV airflow rate estimation of empirical models via data-driven modeling • Achieved high accuracy by customizing models individually for each room • Compared gray-box (physics-informed) with black-box (purely data-driven) models • Assessed data efficiency and model generalization using learning curve analysis • Revealed model robustness and key factors affecting airflow by sensitivity analysis Empirical models are widely used in building design and simulation to estimate airflow rates for single-sided natural ventilation (SSV). However, these models often suffer from limited accuracy. Data-driven modeling offers a viable solution to improve model accuracies by customizing models for various practical scenarios. This study investigates the feasibility and performance of two data-driven modeling approaches for SSV: gray-box modeling (physics-informed using polynomial regressions) and black-box modeling (purely data-driven using machine learning regressions). Taking SSV through a solitary bottom-hung window as a case study, training data were acquired from a full-scale test bench via tracer gas measurements conducted over 11 days. The results indicate that both modeling approaches significantly improve accuracy compared to existing empirical models from the literature. Notably, individual models tailored for each test room generally outperform generalized models developed for all rooms. This finding highlights the importance of customizing models on-site in real-world applications. When comparing the two modeling approaches, gray-box models require lower training ratios to achieve good generalization, demonstrating higher data efficiency than black-box models. Furthermore, sensitivity analysis reveals that gray-box models are more robust and reliable during interpolation and extrapolation, making them particularly suitable for scenarios with limited data availability. Incorporating opening area corrections and selecting root mean square error (RMSE) as the objective function are crucial for achieving optimal gray-box model performance. Overall, this study highlights the potential of physics-informed gray-box data-driven modeling as an advanced strategy for enhancing SSV airflow rate estimation, providing valuable insights to guide future research.

Summary The proper optimisation of fields under waterflooding under uncertainty might require the evaluation of multiple scenarios over a set of reservoir models designed to incorporate geological, structural and stratigraphic uncertainties. Nowadays, reservoir models might have several millions of grid cells and a larger computing infrastructure is needed in order to achieve a near-optimal solution for the net present value objective function given the large uncertainties. In this work a methodology is presented where data-driven models, in the form of capacitance resistance methods, together with analytical fractional flow theory and the help of machine learning techniques are used to perform the optimization of a set of reservoir models under uncertainty. The fractional flow parameters for the Buckley Leverett function are calculated on a well by well basis using iterative ensemble smoothers after a connectivity analysis is performed. The connectivity analysis is further conditioned initially to flow diagnostics averages and to averages for the time of flight. The objective is to maximize the net present value by using proxy models that better match the reservoir and to provide insights on drainage areas and possible infill drilling locations for better field development plans using a fraction of the time required.

Evaluation of the economic benefit of field development projects in the oil and gas industry is naturally subject to subsurface uncertainties and risks. While high economic returns from produced volumes compensated for the lack of robust uncertainty estimates in the past, lower margins in tight market conditions and more complex project designs require increased efforts in delivering reliable uncertainty estimates for improving reservoir management decision support in times to come. An increasing number of field development projects include rigorous uncertainty quantification workflows based on parameterized subsurface uncertainties. Estimation and reduction of prediction uncertainty has no value unless it can potentially influence a decision process. This requires an integrated workflow design across disciplines and a proper risk mitigation plan. This work presents the implementation of an uncertainty workflow in reservoir management with a focus on reservoir modeling and simulation. Practical workflow design steps and intermediate milestones are discussed for generating alternative reservoir model realizations including historical production data. Calibrated model realizations define the basis for prediction estimates of a predefined field development plan. Practical steps are discussed with application to a complex oil rim development for an existing reservoir with long production history. This study aimed at an uncertainty assessment of a field development plan and a proposal for optimized relocations of infill wells considering multiple geological realizations. In conclusion, this work describes examples, experiences, and recommendations for an industry implementation for a risk assessment of a field development plan under subsurface uncertainties.