Acquiring Accurate Real-Time Formation Fluid Properties to Provide In-Situ Fluid Analysis While Drilling

Abstract

Abstract The composition of natural gas and crude oils specifies their calorific and commercial value. To determine required processing steps for intermediate and end products of the reservoir fluid and to define the layout of production facilities, an accurate fluid analysis is vital. Currently, the most reliable way of acquiring fluid compositional data is provided by recovering samples from the downhole environment with dedicated fluid sampling services for surface laboratory analysis. Specialized PVT (Pressure-Volume-Temperature) laboratories provide a detailed fluid characterization using advanced techniques such as gas chromatography (GC). This procedure is very costly and time consuming. Therefore, improved measurement systems and alternative methods with lower cost are commonly requested. One way to hasten the analysis of reservoir fluids and to lower the cost is the introduction of sampling while drilling services. Even though, these products have been commercial for nearly a decade, most sampling jobs are still conducted with wireline services after the well has been drilled, delaying the acquisition, and incurring additional rig costs. One reason that has limited the application of logging-while-drilling (LWD) fluid sampling services is the reduced data resolution in real time. Whereas wireline services are able to transmit comprehensive fluid identification data in seconds, it takes minutes for a comparable LWD service to transmit the data even in significantly reduced resolution. The wireline data transmission rate is many thousand times higher than while drilling. A wired pipe application is available that can overcome this constraint, however, at much higher cost. For a representative fluid evaluation and the selection of the optimal fluid samples for characterization in a dedicated PVT lab, an improved prediction of the fluid properties during pump-out, especially for LWD services, is required. Considering the limited real-time bandwidth available, the evaluation must be performed downhole. Therefore, new fluid type algorithms that combine multiple sensor readings into one model have been developed and implemented in the downhole electronics to provide a more sophisticated and thus conclusive in-situ fluid analysis. This paper will discuss and present the most recent enhancements, achieved by adding machine learning algorithms and chemometrics to LWD fluid sampling services. Models are trained on data from multiple field studies and typical deepwater applications to simplify data interpretation and sample selection, resulting in comprehensive fluid type information being available at surface for real-time decisions and accurate in-situ fluid analysis.