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Declining production and dwindling reserves are a main challenge for many mature fields. For such fields rejuvenation projects are common and may include the use of an integrated operations (IO) or digital oilfield (DOF) system as an asset management decision support tool. Forecasting future production in the short-term is a key part of the asset decision making process. However, in traditional practice with multiple locations and teams modeling in silos, not all production system interactions are considered for short-term decision making. In many cases, short-term production (STP) forecasting is done using a simplistic analytical approach, in which a decline rate is used to forecast, and the forecast is adjusted on a monthly basis. Many assumptions are made, which limits realistic prediction of field potential by ignoring reservoir-network interactions. This can result in large deviations from actual production in fields with secondary or tertiary recovery and preclude the engineers understanding what is actually happening in their production system. The objective of this work was to provide a streamlined methodology for making short-term forecasts using integrated models that help engineers better understand the interactions between different parts of the production system. This would provide a proactive approach to managing the production. To recognize the true potential of the asset, an "integrated asset model" is introduced as part of a DOF or IO project with state-of-the-art modeling techniques that models the asset as a whole unit rather than isolated silos. The integrated asset model framework used in this work includes eight dynamic reservoir models coupled to a production network model and a complex process facilities model. Although such an integrated model is expected to provide improved forecasts, it is can also be time consuming to keep the model updated. To mitigate this, the integrated asset model framework is also supported by an underlying system that keeps the model up-to-date by incorporating the latest production data to reflect current reservoir/production changes making it "live" and always ready for predictions. The model has been modified to support detailed modeling of events and activities. For STP, the workflow automates calculation of well and field potentials as well as planned/unscheduled deferments based on the activity plan and expected production. In addition to making short-term forecasts, the model can also be used to validate production enhancement activities before they are approved and implemented. In this way, the impact of these production enhancement activities can be assessed, not only for the single well, but also for the entire production system. Some of the expected benefits and value gains from this approach are – Improved confidence/accuracy in the forecasts and effectiveness of proposed enhancements.Systematic and streamlined process for short-term forecasting and validating proposed enhancements.Additional information and increased confidence in estimates used to support cost of production enhancements.Identification of the impact of planned short-term activities and production enhancements on the entire asset.Capability to run "what-if" scenarios to improve the effectiveness of planned activities and enhancements.Increased collaboration between different engineers, models and domains for better decision making. This novel approach modernizes perspective for short-term forecasting and reveals that an asset's true production potential can best be predicted using an integrated modeling approach.
Declining production and dwindling reserves are a main challenge for many mature fields. For such fields rejuvenation projects are common and may include the use of an integrated operations (IO) or digital oilfield (DOF) system as an asset management decision support tool. Forecasting future production in the short-term is a key part of the asset decision making process. However, in traditional practice with multiple locations and teams modeling in silos, not all production system interactions are considered for short-term decision making. In many cases, short-term production (STP) forecasting is done using a simplistic analytical approach, in which a decline rate is used to forecast, and the forecast is adjusted on a monthly basis. Many assumptions are made, which limits realistic prediction of field potential by ignoring reservoir-network interactions. This can result in large deviations from actual production in fields with secondary or tertiary recovery and preclude the engineers understanding what is actually happening in their production system. The objective of this work was to provide a streamlined methodology for making short-term forecasts using integrated models that help engineers better understand the interactions between different parts of the production system. This would provide a proactive approach to managing the production. To recognize the true potential of the asset, an "integrated asset model" is introduced as part of a DOF or IO project with state-of-the-art modeling techniques that models the asset as a whole unit rather than isolated silos. The integrated asset model framework used in this work includes eight dynamic reservoir models coupled to a production network model and a complex process facilities model. Although such an integrated model is expected to provide improved forecasts, it is can also be time consuming to keep the model updated. To mitigate this, the integrated asset model framework is also supported by an underlying system that keeps the model up-to-date by incorporating the latest production data to reflect current reservoir/production changes making it "live" and always ready for predictions. The model has been modified to support detailed modeling of events and activities. For STP, the workflow automates calculation of well and field potentials as well as planned/unscheduled deferments based on the activity plan and expected production. In addition to making short-term forecasts, the model can also be used to validate production enhancement activities before they are approved and implemented. In this way, the impact of these production enhancement activities can be assessed, not only for the single well, but also for the entire production system. Some of the expected benefits and value gains from this approach are – Improved confidence/accuracy in the forecasts and effectiveness of proposed enhancements.Systematic and streamlined process for short-term forecasting and validating proposed enhancements.Additional information and increased confidence in estimates used to support cost of production enhancements.Identification of the impact of planned short-term activities and production enhancements on the entire asset.Capability to run "what-if" scenarios to improve the effectiveness of planned activities and enhancements.Increased collaboration between different engineers, models and domains for better decision making. This novel approach modernizes perspective for short-term forecasting and reveals that an asset's true production potential can best be predicted using an integrated modeling approach.
This paper aims to describe the overall EOR GASWAG concept with some of the key findings after first phase execution and some of the measures taken to maintain the project within the planned OPEX to remain economic. Secondly, to describe a comprehensive reservoir management plan which includes a fit for purpose data acquisition plan and more importantly how the remaining challenges are addressed through the RMP optimization to maximize recovery. Finally, this paper outlines the main key challenges to be faced once the injection phase kicks off, highlighting the surveillance and monitoring strategies to overcome them.
The goal for mature fields, is to efficiently close the gap between its existing production and its maximum available capacity. For the mature field offshore Borneo, with timeworn infrastructure, old technology and manual data processing, the big challenge was understanding and analyzing the asset performance. With multiple operational locations, dispersed teams and domain experts working in silos, not all the reservoir-production-facility system interactions were considered for strategic decisions. Amount of time spent in the model updates has not only resulted in limited time for engineering analysis, but also resulted in longer decision cycle time. The lack of model readiness in time to respond has led to reactive decisions rather than proactive asset management. The core challenge was – how to leverage investment in real-time operational data to continuously update discipline-specific models facilitating accurate predictions of key events, possible system upsets, and support engineers to proactively manage their production systems to optimize current production while improving overall recovery. This triggered adoption of an Integrated Asset Modeling (IAM) methodology for end-to-end asset optimization. This was achieved by creating an IAM framework that includes the 8 reservoir simulation models, coupled with a common production network model of around 80 strings integrated with a complex process-facilities model. This new business process is supported by an underlying system that keeps model live/up-to-date with the current reservoir and production changes creating Integrated "Live Asset Model" (LAM) for the asset optimization. IAM approach has resulted in accurate metering, debottlenecking and boosting production operational efficiency. End-to-end surveillance of the system and full understanding of hydrocarbon pathway was the key for successful implementation of IAM for the asset optimization. The technique was not only implemented for the short term planning to improve the production using well intervention & optimization techniques, but also for improving reserves by injection of liquids/gases into the reservoir or Enhanced Oil Recovery (EOR) techniques. This case study illuminates the effective use of an IAM approach in the complex mature asset for improved asset production forecasting. Some of the key benefits and early value gains are – Realized the promised reserves expected by the EOR program by optimizing the reserves and production.Delivered integrated solution with a holistic view of the asset, by breaking down the barrier between different disciplines.Accurate estimation of production potential, with a rigorous scientific approach, by integrating reservoir, network, and process models without losing any of the details of the individual models. Promoted collaborative decision making by bringing people, process and technology together via collaborative work environment (CWE)Assessing how the existing surface pipeline network and facilities impact on the overall asset performance.Enhanced development planning by simulating various optimization scenarios and validating the impact of additional/infill wells and quantifying its production gains. The IAM model results emphasize the criticality of such an approach in making decisions for declaring reserves and production profiles throughout field life. It is the first field to implement Integrated "Live Asset Model" concept by automating the relevant time model updates. Leveraging CWE to bring experts across multiple locations, teams and domains for improved quality decisions.
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