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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 173152, “Evaluating Formation-Fluid Properties During Sampling- While-Drilling Operations,” by K. Indo, J. Pop, K. Hsu, and S. Ossia, Schlumberger; G.-L. Atzeni and A. Malossi, Eni; and V. Agarwal, A. Garcia-Mayans, S. Paul, J. Varughese, and S. Haq, Schlumberger, prepared for the 2015 SPE Drilling Conference and Exhibition, London, 17–19 March. The paper has not been peer reviewed.
Recent experience with a newly introduced sampling-while-drilling service has shown that it is possible to make reliable downhole formation-fluid-property estimates during sampling-while-drilling operations. These property and contamination estimates facilitate the management of the entire while-drilling sampling process by aiding sample-capture decisions and allowing the best possible use of the sample bottles currently available on a drilling bottomhole assembly. Moreover, the contamination estimates, together with the real-time fluid-property estimates, enable prediction of the uncontaminated-fluid properties.
If performed during drilling (when filtrate invasion is not fully developed), fluid scanning—where formation fluid is pumped and analyzed within the sampling-while-drilling tool without taking a sample—affords the opportunity to characterize the formation fluid at potential sampling depths before committing sample chambers. The success of such an approach depends on how completely the in-situ fluid can be characterized and how free of contamination the assessed properties are.
By use of downhole optical spectrometry, it has been shown recently that it is possible to estimate in real time at least the following formationfluid properties: fluid color and type, hydrocarbon composition at various levels, carbon dioxide (CO2) content, gas/oil ratio (GOR), formation volume factor (FVF), and asphaltene content. Other measurements such as pressure, temperature, pressure gradients, and fluid density and viscosity enhance the characterization. Although not replacing pressure/volume/temperature (PVT) laboratory measurement of fluid properties, this list allows the fluid to be characterized sufficiently well to determine the fluid type and to differentiate between fluids at different spatial locations. This capability becomes important when deciding which fluid to sample, whether to recover a fluid sample, and whether another sampling run should be undertaken.
The process of differentiating between fluids becomes increasingly difficult as the level of contamination of the sampled fluid increases; the proper basis for comparison is, ideally, a contamination-free (clean) fluid. If estimates of uncontaminated-fluid properties were available during drilling operations, the allocation of samples to fluids having the highest recovery priority would be expedited.
Indo, K.. (Schlumberger) | Pop, J. J. (Schlumberger) | Hsu, K.. (Schlumberger) | Qi, J.. (Schlumberger) | Agarwal, V.. (Schlumberger) | Garcia Mayans, A.. (Schlumberger) | Ossia, S.. (Schlumberger) | Haq, S. A. (Schlumberger) | Varughese, J.. (Schlumberger)
Abstract Over the last two decades downhole fluid analysis (DFA) using visible and near-infrared spectrometry has proven to be one of the most effective means for obtaining accurate and detailed reservoir fluid property information during formation tester operations. In a previous publication (SPE 166464) a methodology was introduced for estimating fluid properties, such as fluid type, hydrocarbon composition (C1, C2, C3, C4, C5, and C6+), carbon dioxide content, and gas/oil ratio (GOR), from downhole optical spectrometer data acquired during sampling operations. We have extended the methodology introduced in the previous publication to the real-time estimation of asphaltene content of black oils. The equation derived for quantifying the asphaltene content of crude oils uses optical densities (OD), the absorption coefficients of asphaltene and resin, stock tank oil (STO) density, resin content and formation volume factor (FVF). In the process of deriving the asphaltene content a new method was devised for estimating FVF from optical data. The unknown parameters in the equation and the uncertainty in the estimate of asphaltene content are calibrated against a database that contains asphaltene content data of various crude oils and the corresponding optical spectra. Using the derived equation, a maximum likelihood estimate of asphaltene content of crude oil and its associated uncertainty can be obtained. The accuracy of the method for estimating FVF and asphaltene contentwas verified and validated using laboratory crude oil data. The method was also applied to downhole optical spectral data acquired during a sampling-while-drilling (SWD) operation. It was found that the estimated asphaltene content and FVF obtained from the measured downhole spectral data showed very good agreement with the results of laboratory pressure/volume/temperature (PVT) analysis performed on captured fluid samples.
Abstract Formation testers are commonly used to obtain fluid samples and measure formation pressure during openhole logging operations. Accurate identification of the produced fluid usually depends on the analysis of the sample chamber contents at the surface. An enhanced fluid characterization technique is now possible using the measurements of in-situ optical fluid density. This multi-phase fluid analysis can be used to determine the type formation fluids and to evaluate significant fluid characteristics. The optical fluid analysis technique utilizes a visible and near-infrared absorption spectrometer for fluid discrimination. The spectrometer measures the light transmittance of a liquid at many different wavelengths and distinguishes between oil and water by comparing the resulting absorption spectra in the visible and near-infrared region. The spectrometer yields quantitative data on specific fluid phase volumes and qualitative data concerning fluid properties. Experiments were performed to catalog the optical fluid density characteristics of typical hydrocarbons, formation waters, filtrates, drilling mud systems, and mixtures of these fluids. Utilizing this data base, optical density measurements can do an excellent job differentiating between oil, oil-base mud, and water. Hydrocarbon responses also show a strong correlation trend with optical fluid density measurements and can be used to estimate the in-situ oil gravity. Furthermore, quantitative differentiation between oil-base drilling fluid filtrates and hydrocarbons is possible. This outcome was observed with diesel, synthetic oil, and various other oil-base filtrates. An enhanced technique has been developed, which performs a weighted regression analysis on the entire optical spectrum, to fully quantify the mud filtrate contamination, water, and formation hydrocarbon mixtures in real time. The advantage of this technique, over utilization of a single wavelength, is the ability to evaluate multi-phase fluids more precisely. Field examples are presented to illustrate the application of the optical density information to the interpretation of formation fluid characteristics. The technique reduces overall sampling time, minimizes sample contamination, improves sample quality, and provides basic characterization of the fluids. Introduction Modern wireline formation testers (WFT) are now designed with improved sampling, pressure, and permeability measurement capabilities. As part of the enhanced sampling capabilities, the use of an enhanced downhole spectroscopy module as shown in Fig. 1 (working in conjunction with the pumpout module) allows the engineer to make intelligent decisions of when and where to take samples.1–3 The optical spectroscopy module is typically placed in the WFT tool string between the probe and the sample chambers or pumpout module. It monitors the fluid flow in the flowline using two or more sensor systems spaced along the flowline; an optical spectrometer and an optical gas detector. The optical spectrometer allows differentiation between various liquids and is the topic of this paper. Differentiation between formation fluids and mud filtrates helps minimize the amount of sample contamination and provides a method for fluid identification.
Rashaid, Mona (Kuwait Oil Company) | Harrison, Christopher (Schlumberger) | Ayyad, Hazim (Schlumberger) | Dumont, Hadrien (Schlumberger) | Smythe, Elizabeth (Schlumberger) | Sullivan, Matthew (Schlumberger) | Meier, John (Schlumberger) | Fukagawa, Shunsuke (Schlumberger) | Miyashita, Masaki (Schlumberger) | Grant, Bill (Schlumberger) | Morikami, Yoko (Schlumberger) | Kajikawa, Yohei (Schlumberger) | Maekawa, Yuichi (Schlumberger) | Tsuboi, Hidenori (Schlumberger)
Abstract The accuracy of the phase envelope calculated for a black oil sample strongly depends upon the quality and type of information used to optimize the equation of state (EOS). Possible inputs for EOS tuning include (but is not limited to) composition from a chromatogram or optical absorbance, density, saturation pressure (bubble or dew point pressure), and the relative volumes of liquid and gas. In this manuscript, we describe a workflow using a system of microsensors that our group has previously published that accurately measures fluid properties from which the phase envelopes of several black oil samples are calculated and refined.
Abstract During formation tester operation, the use of downhole optical spectrometry has been proven 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 since the results may impact 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, retrograde gas and oil. Based on 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 pseudo components, C1, C2, C3, C4, C5 and C6+, and CO2. 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 to 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.