Continuous Well Production Flow Monitoring and Surveillance

Abstract

Abstract In E&P, from asset managers to front end operations staff, there is a common problem - we are data rich, but information poor. In particular, timely well-by-well production surveillance and allocation often remains a problem. Gathering data, even real time data, from wells and facilities hasn't been an issue, but validating the data and relating this data to individual well production rates in a coherent, consistent and timely manner and then taking prompt action, is a challenge. Traditional routine well testing simply provides a series of snap-shots of a well's performance, which may or may not reflect the production during the intervening period. Errors are typically spread across the wells and reservoirs through a reconciliation process comparing estimated well productions and actual metered sales on a weekly or monthly basis. This paper describes the development and application of a new tool, FieldWare* PRODUCTION UNIVERSE * (PU), which estimates real time well production rates from simple field measurements and provides online reconciliation against bulk measurements and export meters. The novel aspect of the technique is that it uses dynamic, data-driven models to describe the production process, together with a new well test methodology for capturing the data to build the initial models. Well tests include a deliberate disturbance to the production to determine the dynamic characteristics of a well. The models do not require underlying physical or process models, predetermined multiphase flow correlations, compositional data or well/piping/equipment details. This has made the models quick to set-up and easy to maintain. FieldWare PRODUCTION UNIVERSE is now fully operational and used for well-by-well production surveillance and monitoring at many of Shell's production facilities worldwide, both onshore and offshore. The application of PU has helped increase production through improved monitoring, resolved hydrocarbon allocation problems through real time reconciliation, allowed an increase in time between well tests and reduced travel to field locations. The availability of real time production data is a key enabler for future smart optimization and intelligent diagnostics. PU is a foundation element for Shell's Smart Fields initiative. Introduction This paper is intended to introduce the Shell's PRODUCTION UNIVERSE project at a high level and share some of the experiences and findings of the first phase of the development. In particular, we wish to highlight the PU application as an example of the innovative synthesis of oil and gas operational and technical expertise and state of the art mathematical techniques. Historically the oil and gas production industry has relied on traditional methods for individual well production flow monitoring and surveillance. This provides periodic well test information using test separators or multiphase meters, sometimes supplemented with real time pressure and temperature data gathered from the well between tests. Since the test separators / multiphase meters are normally shared among a number of wells, the actual performance of a well is only measured periodically or on demand. Typically around 2% of the well monthly production is measured by well testing. Thus the surveillance of individual wells is a periodic discontinuous process. This is not optimal, as many well problems are not detected until a well is re-tested. Well test conditions may be very different from actual operating conditions. This conventional surveillance and monitoring methodology is premised on the concept that oil and gas production systems were largely steady state and these snap shots in time were adequate to manage the business. However in many fields, well performance and plant-operating conditions can change rapidly and there is value in closer and more regular well production surveillance. Furthermore when a field enters a period of rapid production decline, it requires a higher level of attention and higher frequency of data gathering. The inadequacy of the "snap shot" paradigm then becomes even more pronounced.