Machine Learning Approaches for Compositional Fluid Analysis in Logging While Drilling Using Near-Infrared Data

SPE Annual Technical Conference and Exhibition, vol. 31, pp. 6278–6296

Abstract

Abstract Accurate quantification of light hydrocarbon components (C1–C6) in downhole fluids is critical for reservoir evaluation and production planning. This study presents a comparative analysis of nonnegative least squares (NNLS) regression, indirect hard modeling (IHM) and convolutional neural networks (CNNs) for compositional analysis of near-infrared (NIR) spectra of downhole fluids. Quantification of mass fractions of hydrocarbon fractions from methane (C1) over volatile alkanes (C2-C5) to liquid and heavy alkanes(C6+), and of carbon dioxide (CO2) is particularly challenging due to overlapping spectral features associated with aliphatic hydrocarbons. The predictive performance of all methods is evaluated using a curated dataset of NIR spectra from pure and mixed fluid samples. The NNLS model attempts direct linear spectral unmixing; IHM attempts spectral unmixing via a close fit of the measured spectrum; while CNNs are trained end-to-end on augmented spectra mimicking phenomena expected during logging-while-drilling acquisitions. Results indicate that all three methodologies exhibit good accuracy in predicting pure components and mixtures. IHM developed within this works as a mixture-centric model, shows better generalization to complex mixtures while lacking precision on pure components. NNLS and CNNs correctly identify pure components with root mean squared error (RMSE) being well below 5%, with similar accuracy on fluid mixtures. Hence, both NNLS and CNN are promising approaches for real-time, in-situ compositional analysis, offering a scalable solution for embedded downhole applications.

Authors 6

1. Wolfgang Weinzierl

   Baker Hughes (Germany)

   Affiliation as printed

   Baker Hughes, Celle, Lower Saxony, Germany

2. Philipp Schapotschnikow

   Baker Hughes (Germany)

   Affiliation as printed

   Baker Hughes, Celle, Lower Saxony, Germany

3. J. Denninger Aachen

   RWTH Aachen University

   Affiliation as printed

   ITMC, RWTH Aachen, Aachen, North Rhine-Westphalia, Germany

4. Eick Niemeyer

   Baker Hughes (Germany)

   Affiliation as printed

   Baker Hughes, Celle, Lower Saxony, Germany

5. Alina Adams Aachen

   RWTH Aachen University

   Affiliation as printed

   ITMC, RWTH Aachen, Aachen, North Rhine-Westphalia, Germany

6. Ansgar Cartellieri

   Baker Hughes (Germany)

   Affiliation as printed

   Baker Hughes, Celle, Lower Saxony, Germany