Net present value (NPV) and voidage replacement ratio (VRR) are the key drivers to define an optimal reservoir development strategy that maximizes returns while maintaining reservoir health. In the subsurface context, maximizing NPV consists of optimizing the well locations. Voidage replacement ratio (VRR), which is defined as the ratio between the volume of injected fluid and the volume of produced fluid, measures the rate of change in reservoir energy. Conventionally, operators try to maintain a VRR close to one during the whole field life. Typically a single value of VRR is used as a metric to represent the whole reservoir. However, this approach does not capture the lateral variation in pressure seen in giant fields. This paper focuses on a more suitable method for determining the VRR for each user-defined pressure region using reservoir simulation. This method is used to plan the location of future wells during the long term development plan and maximize NPV and recovery. Two scenarios of well location will be examined. The first scenario consists of optimizing well location using a single VRR metric for the whole field. The second scenario uses the VRR from each pressure region to decide on the optimum number of wells per region. This latter approach is shown to give better results in planning well location for future field development and is consistent with the reservoir pressure distribution across the field.
Throughout the previous few years, substantial attention has been paid to the usage of smart well technologies to help improve recovery, particularly with technological improvements and an increasing expanse of opportunities in more challenging and rewarding assets. The fundamental focus has been to propose and develop workflows that integrate several surface/subsurface subprocesses and automate the entire workflow. In cases where significant investment is made to complete smart wells with remotely controlled inflow control valves (ICV), reservoir sweep becomes decisive when evaluating the efficient recovery. Application of this technology has been challenging because it is a modern concept. This study showcases the effective application of ICVs within intelligently completed fields to satisfy the objective function by augmenting reservoir sweep and oil recovery. In this study, a commercial full-physics numerical reservoir simulator has been used to evaluate a synthetic simulation model mimicking a realistic reservoir with waterflood. The wells are installed with smart well completions using ICVs that are controlled by conditional statements called procedures. The decision parameters varied to determine if the level of ICV opening within producer wells is water-cut and well-injection rates. Then, the cumulative oil recovery is used as an objective function to increase the maximum oil recovery. The ultimate goal is to reach the highest net present value (NPV) through having higher cumulative oil production values with the lower water injection and water production rates. The relatively high expenditure linked with installing intelligent completions within wells drive further the importance to apply and study the advantages of this technology on multiple, diverse cases coupled with specifically planned workflows. Recent studies have shown that a robust reservoir management plan along with an effective application of ICVs within intelligently completed fields can augment reservoir sweep and oil recovery. The results of the study demonstrated the positive impact when using ICVs on NPVs calculated compared to the base case where traditional completions have been used. It is also shown that, without a robust reservoir management plan, the use of intelligent completions might not always be successful. Augmenting the performance of the reservoirs, in addition to looking at the individual well performance, forms the crux of a sound reservoir management plan. This study, therefore, examines the big picture by following a field-wide approach rather than focusing solely on individual or near-well performance. The core of this study is to provide a framework of effective integration of data from leading performance indicators attributing to intelligent well completions, with the ultimate goal of optimizing the reservoir recovery.
This paper presents the success of an end-to-end implementation of a Well Integrity Management (WIM) solution that replaced a legacy system, which is no more accessible. This successful implementation involved comprehensive data mapping and migration (direct and/or reverse engineered), roll-out of the well integrity standards, configuration of the well failure model, integration of the standard well integrity workflows and business process management as per the company structure and RACI chart. The overall solution implementation was split into three critical tasks covering data management, solution design & configuration and testing & fixes to deliver faster sprints in a readily usable solution. Issues encountered were related to the lack of legacy system documentation, understanding the data model interrelation, well failure model (WFM) configuration, and the absence of a representative equipment inventory. The lack of legacy system documentation dictated the need to reverse engineer the legacy system data model to carry out a proper data mapping & migration. The WFM is usually based on a representative equipment inventory, and due to its unavailability, a generic but standard equipment inventory was curated to populate all the well barriers in accordance with the well types and other criteria. The WFM rules themselves needed validation and rigorous testing on both the ends, data input & assessment output, in addition to the WFM logic and conditions to assess each well barrier elements. The new solution also needed to reflect current organization layout and approval levels to properly manage the ticket submissions, approvals, and alert notifications. Extensive and constant collaboration of different stakeholders was key to the success of this implementation, ensuring an effective and timely transition from an inactive legacy system to a readily usable comprehensive solution that covers key WIM workflows and more. The WIM Solution was fully & successfully deployed and legacy data was preserved & migrated. The following results were achieved: 1 - Establishment of an industry standard production data model (PDM) to host all WIM related data (legacy & new), 2 - Rollout and automation of integrity assessment workflows (WFM) using business process management tools, 3 - Deployment of an analytics tool, 4 - Interactive dashboards and visualization tools with alerts and notifications to ensure integrity compliance, awareness, and timely response. The ability to rescue, map and migrate a legacy data model with no inherited documentation and little to no guidance was crucial for the success of this implementation. The new solution, built on an open & scalable architecture, opened the pathways to accommodate additional workflows, business rules, dashboards and new data sources without costly workarounds associated with closed and proprietary systems. As a result, it has proved to be an important cogwheel in the organization's digital transformation journey.
Downhole control devices are being widely implemented in fields globally; and, because of the costs involved in their implementation, a robust reservoir performance forecast is necessary. A prerequisite to a sound reservoir development plan is to have a robust history-matched reservoir simulation model. This study involves use of a downhole inflow control device (ICD) well configuration in the reservoir simulation model to perform history matching of a green-field offshore Abu Dhabi. The results of this approach are compared to the results from traditional approaches. The scope of this study is to examine the differences in both history match approaches. Reservoir A is one of the major reservoirs of a green-field located offshore Abu Dhabi, and is being developed with a five-spot water injection pattern. The producers and water injectors are horizontal wells, which are drilled across different flow units within the reservoir. Because the reservoir is heterogeneous across all the flow units, the injection pattern results in a non-uniform water front. The conventional approach to history matching the well performance is to implement a positive skin factor across the well completions to mimic the effect of the inflow control devices (ICDs) installed in the well: increasing the pressure drop (ΔP) between the formation and the well tubing. In this study, the actual downhole configuration was prepared using well-completion analysis software, followed by use of a next-generation reservoir simulator to run the full field reservoir model for the history matching period. As the field is being developed on the principles of digital concept, continuous high-frequency downhole pressure data is available in flowing as well as shut-in conditions. The use of this data, coupled with direct modeling of the ICDs in the simulation model, resulted in a significant improvement in the reliability of the history match, as compared to traditional approaches. This study compares two history matching approaches for fields with wells completed with downhole control devices. The core purpose of this study is to integrate the principles of the digital oil field with conventional history matching techniques, with the ultimate goal of improving the history match.
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