Estimation of Fluid Composition From Downhole Optical Spectrometry

Abstract

Summary During formation-tester operations, the use of downhole optical spectrometry has proved to be essential for reservoir-fluid characterization. Apart from the intrinsic value of fluid profiling, obtaining fluid properties downhole in real time is of particular interest because the results may affect the decision-making process during sampling and ultimately the success of the sampling operation. A new methodology predicts petroleum-fluid composition from optical spectra acquired with wireline or while-drilling formation testers. The method comprises fluid typing, computation of fluid composition, and estimation of data-specific uncertainty. The fluid-typing algorithm is capable of categorizing a sample into three fluid types: gas, gas condensate, and oil. On the basis of the fluid type identified, the appropriate mapping matrix, which transforms optical spectra into compositions, is selected. The mapping matrix is derived from a database consisting of optical spectra, compositions, and pressure/volume/temperature (PVT) properties of a wide variety of petroleum fluids. The outputs of the composition algorithm are the weight fractions of the hydrocarbon pseudocomponents: C1, C2, C3, C4, C5, and C6+, and carbon dioxide. The composition is used to estimate the gas/oil ratio (GOR) by means of an artificial-neural-network algorithm. As a measure of uncertainty, confidence intervals are computed for the predicted components of the composition and GOR. All results are available during acquisition of the data. The accuracy of the algorithm in estimating composition, GOR, and their associated confidence intervals was assessed by comparing the results of the predictions against laboratory-derived results. Several field data sets were analyzed, and the results were compared with the results obtained by PVT laboratories on the same samples. The estimated composition and GOR showed very good agreement with PVT results. Furthermore, the algorithm provides more-accurate estimates of composition and GOR than are available with current downhole optical spectrometers.