Go Fujisawa

Near-Infrared Compositional Analysis of Gas and Condensate Reservoir Fluids at Elevated Pressures and Temperatures

The near-infrared spectroscopic (NIR) analysis of several fluid mixtures approximating natural gases or condensates is reported. Spectra were measured under wide variations of pressure and temperature in accord with conditions found in various gas or condensate reservoirs. Some restrictions simulating currently feasible hardware specifications were placed on spectral data before they were used for analysis. We employed principal components regression (PCR) on inverted Beers Law for compositional analysis. The result shows that it is feasible to conduct an in situ compositional analysis in the reservoir environment. In fact, this algorithm is currently being utilized successfully with an optical spectrometer operating down-hole in oil wells.

Near-Infrared Spectral Analysis of Gas Mixtures

The analysis by near-infrared spectroscopy (NIR) of a series of gas mixtures approximating natural gases is reported. Wide variations of gas pressure and temperature are used in accord with conditions found in various utilitarian gas flow streams. The NIR analysis of CH4 and CO2 composition is found to be straightforward and depends only on compound mass density, but not explicitly on temperature, pressure, or composition. Linearity of the spectra of more complex mixtures is maintained, but the NIR analysis is more complex. Principal component analysis is shown to resolve composition for those gas mixtures.

Coarse and Ultra-Fine Scale Compartmentalization by Downhole Fluid Analysis

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Live Oil Sample Acquisition and Downhole Fluid Analysis

All aspects of crude oil production depend on the proper acquisition and analysis of petroleum samples from subsurface formations. The analyses of hydrocarbon samples that are acquired in exploration wells guide the subsequent production strategies and facility designs. Nevertheless, hydrocarbon sample acquisition and analysis have been subjects of considerable uncertainty, contributing to a frequent inability to predict properly fluid (and volumetric) production parameters. This mismatch can be inordinately costly in important production arenas such as deepwater oil development, thus new solutions are mandated. The recently developed technology Downhole Fluid Analysis (DFA) is significantly improving several problematic processes involving fluid samples. The technology is sufficiently cost-effective to be routinely utilized in many economic settings. DFA can identify unwanted phase transitions and determine unacceptable contamination levels, thus is indispensable for sample acquisition. In addition, DFA can readily identify fluid differences at various points in the reservoir (those that have been penetrated by a well), thus DFA can uncover fluid complexities that are now appreciated to be common. It has long been recognized that fluid analysis can identify different reservoir production units known as compartments. However, the geoscientist has been greatly impeded in this quest due to the high cost of essentially blind sample acquisition. DFA is the “missing link” in this chain, enabling identification of the samples that are needed for characterization of reservoir architecture. Finally, as in forensic science, proof of sample provenance and validity is a quintessential objective, but which has been largely absent in the oil field. DFA has enabled implementation of the “chain of custody” for reservoir hydrocarbon samples. The chain of custody provides quantitative proof that even delicate hydrocarbon samples that travel thousands of miles and to other continents for analysis in fact reflect virgin reservoir fluid properties. This chapter delineates aspects of the setting and the technology for sample acquisition. DFA is introduced in concept and in specific

Optical Sensors for the Exploration of Oil and Gas

There is a large variety of sensing devices for the exploration of oil and gas. These sensors must operate in exceedingly challenging environments, such as high temperatures and high pressures. The majority of sensors are based on electronics because of the maturity of the technology, but optical sensors have been used successfully for specific measurements where no replacement technology exists. We introduce distributed optical sensing and downhole optical spectroscopy, and their unique measurements and value for the exploration of oil and gas are explained.

Uncertainty Analysis of Visible and Near-Infrared Data of Hydrocarbons

Measurement of physical and chemical properties of hydrocarbons plays an important role in the exploration and production of oil wells. In situ measurement of chemical properties of hydrocarbons makes use of visible and near-infrared (vis-NIR) absorption spectra of hydrocarbons. Uncertainty analysis of these fluid properties is central to developing a fundamental understanding of the distribution of hydrocarbons in the reservoir. In this manuscript, we describe an algorithm called the fluid comparison algorithm (FCA), which provides a statistical framework to quantify and compare hydrocarbon fluid properties and associated uncertainties derived from vis-NIR measurements. The inputs to FCA are the magnitude and uncertainty of vis-NIR spectroscopy data of two hydrocarbons. The output of FCA is a probability that two fluids are statistically different. FCA lays the foundations for subsequent optimization and capture of representative reservoir hydrocarbons. Furthermore, in some circumstances, it can also enable real-time decisions to identify reservoir compartmentalization and hydrocarbon composition gradients in natural oil reservoirs.

Reservoir Evaluation by DFA Measurements and Thermodynamic Analysis

Downhole fluid analysis (DFA ) has enabled the cost-effective measurement in oil wells of a variety of chemical properties of reservoir crude oils. An immediate benefit of DFA is the improvement of the sample quality of the reservoir fluid in the subsurface environment. In addition, this early feedback on the nature of the reservoir fluid aids in understanding key reservoir challenges. DFA also enables the accurate determination of fluid gradients in the reservoir in both vertical and lateral directions. These gradients can then be analyzed in a thermodynamic equation of state (EoS ) context; the gas-liquid properties can be modeled with the cubic EoS and the asphaltene gradients equilibrium can be modeled with the Flory–Huggins–Zuo (FHZ ) EoS with its reliance on the Yen–Mullins model of asphaltenes. Time-dependent processes in geologic time can be modeled by adding appropriate dynamic terms to the EoS. Simple thermodynamic models can then be used to understand distributions of key fluid properties for reservoir crude oils and aid in simulating production. This thermodynamic analysis of the geodynamics of reservoir fluids fills a gap in the industrys modeling of reservoir fluids. Traditional basin modeling predicts what fluids enter the reservoir. This new geodynamic modeling coupled with DFA measurements determines what transpired in geologic time in regards to fluid distributions within the reservoir. The output of this fluid geodynamic modeling can then be used as input for traditional reservoir simulation for production. This new understanding of reservoir fluid geodynamics is made possible by new DFA measurements coupled with new FHZ EoS with the Yen–Mullins model.