Estimation of Fluid Composition From Downhole Optical Spectrometry

SPE Journal ◽  
2015 ◽  
Vol 20 (06) ◽  
pp. 1326-1338 ◽  
Author(s):  
Kentaro Indo ◽  
Kai Hsu ◽  
Julian Pop

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.

Author(s):  
Dagfinn Mæland ◽  
Lars E. Bakken

Abstract Based on experience from wet gas compressor testing at NTNU (low-pressure air-water fluid) and K-Lab full-scale testing (normal operating conditions, high pressure and hydrocarbon fluids), this paper documents important aspects relating to the uncertainty evaluation of wet gas compressor performance test results. The Monte Carlo method for evaluation of uncertainty on a wet gas compressor system is outlined, and the resulting uncertainties of key compressor performance parameters are presented. Furthermore, a sensitivity analysis has been performed to evaluate how uncertainties in the output of the model can be appointed to different sources of uncertainty in the inputs, thus identifying main contributors to the uncertainties. The importance of accurately determining the fluid composition, the properties of the fluid components and how these affect the fluid characterization are discussed. Together with the choice of equation of state, the characterization directly affects the simulated fluid properties, and great care is required to obtain reliable compressor performance results. Uncertainties of physical properties originating from the thermodynamic simulation show that compressor power and gas density ideally should be determined from direct measurements and not from thermodynamic simulations.


2008 ◽  
Vol 11 (06) ◽  
pp. 1107-1116 ◽  
Author(s):  
Chengli Dong ◽  
Michael D. O'Keefe ◽  
Hani Elshahawi ◽  
Mohamed Hashem ◽  
Stephen M. Williams ◽  
...  

Summary Downhole fluid analysis (DFA) has emerged as a key technique for characterizing the distribution of reservoir-fluid properties and determining zonal connectivity across the reservoir. Information from profiling the reservoir fluids enables sealing barriers to be proved and compositional grading to be quantified; this information cannot be obtained from conventional wireline logs. The DFA technique has been based largely on optical spectroscopy, which can provide estimates of filtrate contamination, gas/oil ratio (GOR), pH of formation water, and a hydrocarbon composition in four groups: methane (C1), ethane to pentane (C2-5), hexane and heavier hydrocarbons (C6+), and carbon dioxide (CO2). For single-phase assurance, it is possible to detect gas liberation (bubblepoint) or liquid dropout (dewpoint) while pumping reservoir fluid to the wellbore, before filling a sample bottle. In this paper, a new DFA tool is introduced that substantially increases the accuracy of these measurements. The tool uses a grating spectrometer in combination with a filter-array spectrometer. The range of compositional information is extended from four groups to five groups: C1, ethane (C2), propane to pentane (C3-5), C6+, and CO2. These spectrometers, together with improved compositional algorithms, now make possible a quantitative analysis of reservoir fluid with greater accuracy and repeatability. This accuracy enables comparison of fluid properties between wells for the first time, thus extending the application of fluid profiling from a single-well to a multiwall basis. Field-based fluid characterization is now possible. In addition, a new measurement is introduced--in-situ density of reservoir fluid. Measuring this property downhole at reservoir conditions of pressure and temperature provides important advantages over surface measurements. The density sensor is combined in a package that includes the optical spectrometers and measurements of fluid resistivity, pressure, temperature, and fluorescence that all play a vital role in determining the exact nature of the reservoir fluid. Extensive tests at a pressure/volume/temperature (PVT) laboratory are presented to illustrate sensor response in a large number of live-fluid samples. These tests of known fluid compositions were conducted under pressurized and heated conditions to simulate reservoir conditions. In addition, several field examples are presented to illustrate applicability in different environments. Introduction Reservoir-fluid samples collected at the early stage of exploration and development provide vital information for reservoir evaluation and management. Reservoir-fluid properties, such as hydrocarbon composition, GOR, CO2 content, pH, density, viscosity, and PVT behavior are key inputs for surface-facility design and optimization of production strategies. Formation-tester tools have proved to be an effective way to obtain reservoir-fluid samples for PVT analysis. Conventional reservoir-fluid analysis is conducted in a PVT laboratory, and it usually takes a long time (months) before the results become available. Also, miscible contamination of a fluid sample by drilling-mud filtrate reduces the utility of the sample for subsequent fluid analyses. However, the amount of filtrate contamination can be reduced substantially by use of focused-sampling cleanup introduced recently in the next-generation wireline formation testers (O'Keefe et al. 2008). DFA tools provide results in real time and at reservoir conditions. Current DFA techniques use absorption spectroscopy of reservoir fluids in the visible-to-near-infrared (NIR) range. The formation-fluid spectra are obtained in real time, and fluid composition is derived from the spectra on the basis of C1, C2-5, C6+, and CO2; then, GOR of the fluid is estimated from the derived composition (Betancourt et al. 2004; Fujisawa et al. 2002; Dong et al. 2006; Elshahawi et al. 2004; Fujisawa et al. 2008; Mullins et al. 2001; Smits et al. 1995). Additionally, from the differences in absorption spectrum between reservoir fluid and filtrate of oil-based mud (OBM) or water-based mud (WBM), fluid-sample contamination from the drilling fluid is estimated (Mullins et al. 2000; Fadnes et al. 2001). With the DFA technique, reservoir-fluid samples are analyzed before they are taken, and the quality of fluid samples is improved substantially. The sampling process is optimized in terms of where and when to sample and how many samples to take. Reservoir-fluid characterization from fluid-profiling methods often reveals fluid compositional grading in different zones, and it also helps to identify reservoir compartmentalization (Venkataramanan et al. 2008). A next-generation tool has been developed to improve the DFA technique. This DFA tool includes new hardware that provides more-accurate and -detailed spectra, compared to the current DFA tools, and includes new methods of deriving fluid composition and GOR from optical spectroscopy. Furthermore, the new DFA tool includes a vibrating sensor for direct measurement of fluid density and, in certain environments, viscosity. The new DFA tool provides reservoir-fluid characterization that is significantly more accurate and comprehensive compared to the current DFA technology.


2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.


Author(s):  
Craig R. Davison ◽  
Jeff W. Bird

The development and evaluation of new diagnostic systems requires statistically-based methods to measure performance. Various metrics are in use by developers and users of diagnostic systems. Current metrics practices are reviewed, including receiver operating characteristics, confusion matrices, Kappa coefficients and various entropy techniques. A set of metrics is then proposed for assessment of diverse gas path diagnostic systems. The use of bootstrap statistics to compare metric results is developed, and demonstrated for a set of hypothetical data sets with a range of relevant characteristics. The bootstrap technique allows the expected range of the metric to be assessed without assuming a probability distribution. A method is proposed to develop confidence intervals for the calculated metrics. The application of a confidence interval could prevent a good diagnostic technique being discarded because of a lower value metric in one test instance. The strengths and weaknesses of the various metrics with derived confidence intervals are discussed. Recommendations are made for further work.


2020 ◽  
Vol 68 (3) ◽  
pp. 949-964
Author(s):  
Dimitris Bertsimas ◽  
Bradley Sturt

The bootstrap method is one of the major developments in statistics in the 20th century for computing confidence intervals directly from data. However, the bootstrap method is traditionally approximated with a randomized algorithm, which can sometimes produce inaccurate confidence intervals. In “Computation of Exact Bootstrap Confidence Intervals: Complexity and Deterministic Algorithms,” Bertsimas and Sturt present a new perspective of the bootstrap method through the lens of counting integer points in a polyhedron. Through this perspective, the authors develop the first computational complexity results and efficient deterministic approximation algorithm (fully polynomial time approximation scheme) for bootstrap confidence intervals, which unlike traditional methods, has guaranteed bounds on its error. In experiments on real and synthetic data sets from clinical trials, the proposed deterministic algorithms quickly produce reliable confidence intervals, which are significantly more accurate than those from randomization.


Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. D625-D641 ◽  
Author(s):  
Dario Grana

The estimation of rock and fluid properties from seismic attributes is an inverse problem. Rock-physics modeling provides physical relations to link elastic and petrophysical variables. Most of these models are nonlinear; therefore, the inversion generally requires complex iterative optimization algorithms to estimate the reservoir model of petrophysical properties. We have developed a new approach based on the linearization of the rock-physics forward model using first-order Taylor series approximations. The mathematical method adopted for the inversion is the Bayesian approach previously applied successfully to amplitude variation with offset linearized inversion. We developed the analytical formulation of the linearized rock-physics relations for three different models: empirical, granular media, and inclusion models, and we derived the formulation of the Bayesian rock-physics inversion under Gaussian assumptions for the prior distribution of the model. The application of the inversion to real data sets delivers accurate results. The main advantage of this method is the small computational cost due to the analytical solution given by the linearization and the Bayesian Gaussian approach.


2015 ◽  
Vol 18 (03) ◽  
pp. 303-317 ◽  
Author(s):  
D.. Galvan ◽  
G.. McVinnie ◽  
B.. Dindoruk

Summary The Perdido development is one of the most-complex deepwater projects in the world. It is operated by Shell in partnership with Chevron and BP. It currently produces hydrocarbons from 12 subsea wells penetrating four separate reservoirs. The properties of produced fluid vary per reservoir as well as spatially. The producing wells display a relatively wide range of fluid gravities, between 17 and 41 °API, and producing gas/oil ratios (GORs), between 480 and 3,000 scf/bbl. The fluids produced from the subsea wells are blended in the subsea system and lifted to the topside facilities by means of five seabed caisson electrical submersible pumps. In the topside facility, gas and oil are separated, treated, and exported by means of dedicated subsea pipelines. The fluid compositions and properties across the various elements of the production system are used as input data to the respective simulation models, and the corresponding outcomes (e.g., fluid properties, compositions) vary upon the well/caisson lineup and daily operating conditions. Given the wide spectrum of fluids produced through the Perdido spar, a special equation-of-state (EOS) characterization of the fluids had to be developed. Because a common EOS model was used to characterize the fluids, we will call this the unified fluid model (UFM) throughout this study. This approach enables accurate and efficient prediction of the properties of blended fluids and is suitable for use in an integrated-production system model (IPSM) that connects reservoirs, wells, subsea-flowline networks, and topside-facilities models. Such a modeling scheme enables effective integration among relevant engineering disciplines and can represent production and fluid data from field history with high confidence. The IPSM uses a black-oil fluid description for the well and subsea-flowline network models. By use of the initial composition and producing GOR of each well, the fluid composition is estimated by means of a simple delumping scheme. The resulting composition is tracked through the subsea network to the topside-facilities model, where compositional flash calculations are performed. The IPSM can forecast production rates together with fluid properties and actual oil- and gas-volumetric rates across the whole production system. The model can be used to optimize production under constrained conditions, such as limited gas-compression capacity or plateau oil production.


Geophysics ◽  
2003 ◽  
Vol 68 (5) ◽  
pp. 1470-1484 ◽  
Author(s):  
Alastair M. Swanston ◽  
Peter B. Flemings ◽  
Joseph T. Comisky ◽  
Kevin D. Best

Two orthogonal preproduction seismic surveys and a regional seismic survey acquired after eight years of production from the Bullwinkle field (Green Canyon 65, Gulf of Mexico) reveal extraordinary seismic differences attributed to production‐induced changes in rock and fluid properties. Amplitude reduction (of up to 71%) occurs where production and log data show that water has replaced hydrocarbons as the oil–water contact moved upward. Separate normalizations of these surveys demonstrate that time‐lapse results are improved by using seismic surveys acquired in similar orientations; also, clearer difference images are obtained from comparing lower‐frequency data sets. Superior stratigraphic illumination in the dip‐oriented survey relative to the strike‐oriented surveys results in nongeological amplitude differences. This documents the danger of using dissimilar baseline and monitor surveys for time‐lapse studies.


2009 ◽  
Vol 12 (05) ◽  
pp. 793-802 ◽  
Author(s):  
P. David Ting ◽  
Birol Dindoruk ◽  
John Ratulowski

Summary Fluid properties descriptions are required for the design and implementation of petroleum production processes. Increasing numbers of deep water and subsea production systems and high-temperature/high-pressure (HTHP) reservoir fluids have elevated the importance of fluid properties in which well-count and initial rate estimates are quite crucial for development decisions. Similar to rock properties, fluid properties can vary significantly both aerially and vertically even within well-connected reservoirs. In this paper, we have studied the effects of gravitational fluid segregation using experimental data available for five live-oil and condensate systems (at pressures between 6,000 and 9,000 psi and temperatures from 68 to 200°F) considering the impact of fluid composition and phase behavior. Under isothermal conditions and in the absence of recharge, gravitational segregation will dominate. However, gravitational effects are not always significant for practical purposes. Since the predictive modeling of gravitational grading is sensitive to characterization methodology (i.e., how component properties are assigned and adjusted to match the available data and component grouping) for some reservoir-fluid systems, experimental data from a specially designed centrifuge system and analysis of such data are essential for calibration and quantification of these forces. Generally, we expect a higher degree of gravitational grading for volatile and/or near-saturated reservoir-fluid systems. Numerical studies were performed using a calibrated equation-of-state (EOS) description on the basis of fluid samples taken at selected points from each reservoir. Comparisons of measured data and calibrated model show that the EOS model qualitatively and, in many cases, quantitatively described the observed equilibrium fluid grading behavior of the fluids tested. First, equipment was calibrated using synthetic fluid systems as shown in Ratulowski et al. (2003). Then real reservoir fluids were used ranging from black oils to condensates [properties ranging from 27°API and 1,000 scf/stb gas/oil ratio (GOR) to 57°API and 27,000 scf/stb GOR]. Diagnostic plots on the basis of bulk fluid properties for reservoir fluid equilibrium grading tendencies have been constructed on the basis of interpreted results, and sensitivities to model parameters estimated. The use of centrifuge data was investigated as an additional fluid characterization tool (in addition to composition and bulk phase behavior properties) to construct more realistic reservoir fluid models for graded reservoirs (or reservoirs with high grading potential) have also been investigated.


2020 ◽  
Author(s):  
Heidi S. Christensen ◽  
Jens Borgbjerg ◽  
Lars Børty ◽  
Martin Bøgsted

Abstract Background To assess the agreement of continuous measurements between a number of observers, Jones et al. introduced limits of agreement with the mean (LOAM) for multiple observers, representing how much an individual observer can deviate from the mean measurement of all observers. Besides the graphical visualisation of LOAM, suggested by Jones et al., it is desirable to supply LOAM with confidence intervals and to extend the method to the case of multiple measurements per observer.Methods We reformulate LOAM under the assumption the measurements follow an additive two-way random effects model. Assuming this model, we provide estimates and confidence intervals for the proposed LOAM. Further, this approach is easily extended to the case of multiple measurements per observer.Results The proposed method is applied on two data sets to illustrate its use. Specifically, we consider agreement between measurements regarding tumour size and aortic diameter. For the latter study, three measurement methods are considered. Conclusions The proposed LOAM and the associated confidence intervals are useful for assessing agreement between continuous measurements.


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