Publications

Early fluid characterization plays a critical role in the economics of exploitation and deliverability, be it exploitation in the Continental Shelf or deepwater assets. Each fluid has its own characteristics and properties. Fluid composition uncertainties can induce cost overruns in completions and can present surface design challenges. Challenges in the use of synthetic oil-based mud (SOBM) for drilling in reservoirs that contain fresh water and heavy oil increase the risk of not being able to identify the fluid correctly and missing potential pay. The conventional pump-out formation tester can acquire the physical properties of formation fluids. Nevertheless, looking at the fluids physical properties is not enough to determine the formation fluid type since some fluids have very similar physical properties. This paper addresses these challenges with the combination of the fluid density sensor and a recently introduced optical fluid sensor technology that employs optical regression techniques to identify the constituents making up the fluid. The optical fluid sensor uses the mathematics of pattern recognition designed directly into an optical computer, which extracts information from light. Pattern recognition techniques can be applied to optical spectra of complex mixtures for the purpose of detecting the presence of specific compounds, measuring their concentrations, or estimating properties that depend on sample chemistry. This paper presents the successful application of this technology in a complex environment in Indonesia. Fluid characterizations that were conducted by measuring gas/oil ratio (GOR), C1, C2, C3, saturates, and aromatics are presented. The paper also details best practices and tool combinations addressing different reservoir fluids, wellbore deviations, and combinations in pumping directions.