Sand production remains a key technical challenge in the ACG (Azeri-Chirag-Gunashli) field as the target formation is comprised of weakly consolidated sandstone. Although sand control completions (such as open hole gravel pack) are used to limit sand entry into the well, water breakthrough, increased fines production, high flux across completed intervals amongst other factors may negatively impact on their effectiveness as well as stability of reservoir rocks, resulting in high sand production, consequently requiring choking back of wells. This, at times, leads to significant production deferrals, which are attributed to the impact on entire production system: completion, wellbore, chokes, flow lines and production vessels. Therefore, improving techniques and developing technologies for downhole diagnostic and remediation to restore production will be of value throughout the ACG field life. An effective remediation requires an understanding of the sand entry points. Over the last 24 months, BP has developed a new real-time technology solution that employs novel signal processing techniques using Distributed Acoustic Sensing (DAS) systems to detect sand entry points along the wellbore during production. The technology solution has been employed to the interpretation of over 30 conducted surveys to identify sand entry zones in real time. In some instances, the results have also been used to inform targeted remediation using expandable patches. This paper summarizes the results from some of the DAS sand detection surveillance data acquired (in integration with other relevant dataset) and its use in remediation of compromised completion intervals. The authors will also share examples of drawdown optimization and risk management of wells with sand production based on acquired DAS data. This paper will cover a few examples of DAS survey data acquired both for wells with pre-installed fibre (25 wells) and those without it where the technology was deployed via wireline intervention (5 wells). The application of DAS technology in ACG has provided substantial value to date (both in terms of production and safety) and has much more value to be realized as the technology continues to mature.

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This paper shares the results from the first successful deployment of a real-time, multiphase inflow profiling technology applied to BP operated Clair Ridge asset. Uncertainty around distribution and the dynamic behaviour of the fractured reservoir required the deployment of Distributed Fibre Optic Sensing (DFOS) capabilities as part of the well completions across a selection of the well stock, to enable in-depth real-time flow surveillance and maximize recovery from the field. Unlike conventional wireline conveyed sensors and logs that provide static measurements of flow conditions, the DFO data will be used to provide a more comprehensive, dynamic inflow distribution across multizone completion uniquely including flow in the annular space behind blank sections. This paper summarizes key findings from the first deployment of a new real-time technology solution that employs novel signal processing techniques using the DFO data as key sensor inputs to detect relative inflow rates of different fluid types along the wellbore during production. The solution presents inflow logs for each fluid phase in near real-time through proprietary streaming analytics capabilities embedded in a cloud-based software solution. This facilitates 24×7, real-time flow surveillance across wells equipped with fibre in the field. This paper also presents early results from the use of the technology on the first platform drilled production well and discusses how these real-time insights have been effectively applied to provide significant business value, including but not limited to new well start-up optimization, well management and zonal fluid inflow allocation. The paper also detail the use of the technology for water inflow detection and quantification and discusses the implementation of drawdown optimization strategies based on the insight to control water inflow that have already resulted in significant production benefits.

This paper introduces a novel technique that allows real-time injection monitoring with distributed fiber optics using physics-informed machine learning methods and presents results from Clair Ridge asset where a cloud-based, real-time application is deployed. Clair Ridge is a structural high comprising of naturally fractured Devonian to Carboniferous continental sandstones, with a significantly naturally fractured ridge area. The fractured nature of the reservoir lends itself to permanent deployment of Distributed Fiber Optic Sensing (DFOS) to enable real-time injection monitoring to maximise recovery from the field. In addition to their default limitations, such as providing a snapshot measurement and disturbing the natural well flow with up and down flowing passes, wireline-conveyed production logs (PL) are also unable to provide a high-resolution profile of the water injection along the reservoir due to the completion type. DFOS offers unique surveillance capability when permanently installed along the reservoir interface and continuously providing injection profiles with full visibility along the reservoir section without the need for an intervention. The real-time injection monitoring application uses both distributed acoustic and temperature sensing (DAS & DTS) and is based on physics-informed machine learning models. It is now running and available to all asset users on the cloud. So far, the application has generated high-resolution injection profiles over a dozen multi-rate injection periods automatically and the results are cross-checked against the profiles from the warmback analyses that were also generated automatically as part of the same application. The real-time monitoring insights have been effectively applied to provide significant business value using the capability for start-up optimization to manage and improve injection conformance, monitor fractured formations and caprock monitoring.