bluuebunny/crossref_metadata_2025_split huggingface dataset:
History Matching of the PUNQ-S3 Reservoir Model Using the Ensemble Kalman Filter
Authors: Gu Yaqing , Oliver Dean S. 
Published: December 2007

Abstract
The problem of reservoir characterization through automatic history matching has been extensively studied in recent years. Efficient applications have, however, required either an adjoint or a gradient simulator method to compute the gradient of the objective function or a sensitivity coefficient matrix for the minimization. Both computations are expensive when the number of model parameters or the number of observation data is large. The codes for gradient-based history matching methods are also complex and time-consuming to write.
This paper reports the use of the Ensemble Kalman Filter (EnKF) for automatic history matching. EnKF is a Monte Carlo method, in which an ensemble of reservoir models is used. The correlation between reservoir response (e.g. watercut and rate) and reservoir variables (e.g. permeability and porosity) can be estimated from the ensemble. An estimate of uncertainty in future reservoir performance can also be obtained from the ensemble.
The methodology of EnKF consists of a forecast step and an assimilation step. A finite-difference, 3-D, 3-phase black-oil reservoir simulator is used for stepping forward the reservoir states. However, unlike the traditional history matching, the source code of the reservoir simulator is not required, which allows this method to be used with any reservoir simulator. Moreover, this forward step is well suited for parallel computation since the time evolution of ensemble reservoir models are independent, hence the ensemble of reservoir models can be advanced in time simultaneously using multiple processors. Only the data assimilation step, i.e. the computation of Kalman filter, requires communication between processors.
The assimilation of the data in EnKF is done sequentially rather than simultaneously as in traditional history matching. By so doing the reservoir models are always kept up-to-date, which is important and practical when the frequency of data is fairly high as, for example, the data from permanent sensors.
The PUNQ-S3 reservoir model is used to test the method in this paper. It is a small-size (19 × 28 × 5) reservoir engineering model that was developed by a group of companies, institutes and universities in the European Union to compare methods for quantifying uncertainty assessment in history matching. One conclusion is that EnKF can sometimes provide satisfactory history matching results while requiring less computation work than traditional methods.
Integrating NMR With Other Petrophysical Information to Characterize a Turbidite Reservoir
Authors: Dastidar Rahul , Rai Chandra , Sondergeld Carl 
Published: December 2007

Abstract
Uncertainty lies in developing the nation's discovered oil and gas fields. Since the subsurface cannot be accessed directly, indirect methods and procedures must be developed to estimate the size and productive potential of a reservoir. The value of individual pieces of information is realized when they are integrated with other sources of information to characterize a reservoir. New and improved technologies e.g., Nuclear Magnetic Resonance1 (NMR), has been extensively used to determine the reservoir properties such as lithology independent porosity, permeability, water saturation, irreducible water saturation etc.
The Free Fluid Model2 and the Mean T2 Model3 are two NMR based methods to estimate permeability. Partitioning of pore fluid into free and bound volume depends on the precise value of transverse relaxation time (T2), T2cutoff. Infact, for a water wet rock, bound volume irreducible (BVI) is the amount of water that coats the surface of the pore space and can be referred as the irreducible water saturation (Swirr).
In this study fifteen plug samples from a turbidite reservoir (Grubb formation) from California were used. The plugs are classified as arkosic-lithic sandstones (with an average mineral composition of feldspars (52%), quartz (40%), clays (4%), carbonates (3%) and others (1%)). The porosity ranges from 6% to 19% with an average of 15% and the permeability ranges from 0.045 md to 30.42 md having an average of 9.7 md. NMR spectra were collected on saturated (25,000 ppm NaCl brine) and desaturated samples.
Using the cutoff BVI (CBVI4) technique for estimating the percentage of bound water, the measured T2cutoff for the fifteen plugs ranged from 5 msec to 15 msec. The computed free fluid index (FFI) based on the above T2cutoff range is approximately 30% to 50% higher than the FFI calculated from industry recommended T2cutoff value of 33 msec for clastic rocks. For this reservoir, the Mean T2 Model3 gives better estimate of permeability as compared to those calculated using the Free Fluid model2. The irreducible water saturation (Swirr) decreases with increase in permeability. The comparison of Swirr from capillary pressure measurement and centrifuge experiments showed a pattern that can be explained on the basis of mineralogy and microporosity. The main purpose of establishing a correlation between NMR T2 distributions with mercury injection capillary pressure (MICP) is to generate the capillary pressure curves directly from NMR relaxation time distribution during wireline logging. An average value of effective surface relaxivity (ρe) of 24.3 μm/sec has been established for the Grubb formation, which is then used to generate the pseudo capillary pressure curves. The NMR derived capillary pressure curve shows a reasonable match with the air-brine capillary pressure curves derived from MICP.
Wavelets in Petroleum Industry: Past, Present and Future
Authors: Guan L. , Du Y. , Li L. 
Published: December 2007

Abstract
Wavelet is first introduced by Alfred Haar in 1910, but the development of wavelet was very slow until the middle of 1980s. In 1984, Jean Morlet made the wavelet rebirth by developing a new way of analyzing the seismic signals to overcome the deficiencies in the Fourier method. Since then, the new developments of wavelets have fascinated the scientific and engineering communities.
A wavelet is a waveform of effectively limited duration that has an average value of zero and wavelets are a family of basis functions, which can separate a signal into distinct frequency packets that are localized in the time domain. Thus, wavelets are well suited to analyze nonstationary data. They can smooth the basic signals and keep the details of basic signals. Therefore, they provide a multiresolution framework for data representation.
Wavelet analysis is a rapidly developing area in many disciplines of science and engineering and it is used in a wide variety of applications in the areas of medicine, biology, data compression, etc. In recent years, wavelet analysis has found its application in the petroleum industry. This paper reviews the recent application of wavelet analysis in the industry. Various application examples are discussed, especially the examples in the areas of reservoir characterization, geological model upscaling, and well testing.
In Well Nuclear Magnetic Resonance (NMR) Multiphase Flowmeter in the Oil and Gas Industry
Authors: Ong J. T. , Oyeneyin M. B. , Coutts E. J. , MacLean I. M. 
Published: December 2007

Abstract
In the current climate of production monitoring, the main concern is to obtain measurement that has high accuracy and specificity. This can be achieved using a system that is independent of flow regime, having the ability to differentiate the velocity and mass flowrates of at least three phases; gas, oil and water. In addition, it should be capable of quantifying the volume fraction of each phase with respect to the overall flow volume. Currently, there is no single flowmeter that can reliably perform these tasks.
The design of an in-well flowmeter has to account for the electronic components susceptibility to high pressure (P) and temperature (T) by equipping the system with PT sensors. By monitoring the environment, the pressure and temperature are maintained below pre-defined thresholds. This ensures a higher level of accuracy in the measurements as well as prolonging instrument lifetime.
The principle of flow measurement adopted is based on nuclear magnetic resonance (NMR). NMR is suited to flow evaluation in this industry due to the fluids present, i.e. water and hydrocarbon (gas or liquid). These fluids contain 1H and 13C nuclei which are important to NMR measurements. Unlike conventional flowmeters which measure the physical characteristics of the fluid, NMR monitors the molecular environment of the fluid. NMR has the ability to distinguish the different phases through the use of intrinsic molecular properties, i.e. relaxation times and resonant frequencies.
In NMR, the volume fraction is calculated from the proton density. The flow measurement is obtained by monitoring the magnetised nuclei flow into a detection zone. The velocity distribution function is then calculated by applying an inverse algorithm to the data.
The advantages of this system are its ability to be used as a single self-sufficient instrument and its phase independence for up to a two phase flow. This flow-independent measurement technique could be extended for use in three and four (solid) phase flow.
Probabilistic Approach Takes You Back to Reality
Authors: Macary S. 
Published: December 2007

Abstract
One of the most important outputs of the static model is the volumetrics of the original hydrocarbon in place (OHIP). According to the type of reservoir driving mechanism, the reservoir engineer can assume the recovery factor and hence the recoverable reserves. Most simulation studies are being conducted on fields considering a period of production performance to determine the remaining reserves and the best scenario of development to produce these reserves, which is the main function of the dynamic model.
The volumetrics could be approached using a Monte Carlo technique, the end product of which is a range of probable OHIP and, consequently using the proper recovery factor, getting probability distribution of the recoverable reserves. Therefore, using the probabilistic approach is superior in green fields rather than brown fields because it captures the full range of reality and where models are not yet calibrated to dynamic data.
Sometimes, the dynamic data may take a surprise turn from the geological understanding. Different realizations, even the crazy ones, may help under the probability distribution functions in defining some certainties that have not yet even been imagined. In addition, each of the dynamic model's scenarios yields extra recovery, the certainty of which can be evaluated using the probability distribution curve of the remaining reserves. This certainty has direct influence on the economics approval of one scenario over the others.
The current work introduces actual case histories demonstrating how the probabilistic approach could be of real help in selecting the production enhancement scenario and how this tool also could be used to prioritize management interests.
Joint Estimation of Porosity and Saturation Using Stochastic Rock Physics Modeling
Authors: Bachrach R. , Dutta N. 
Published: December 2007

Abstract
Porosity and saturation are fundamental sedimentary properties that affect both seismic wave propagation and appraisal development and production of an oil field. Traditionally, these parameters have been estimated from seismic impedances and velocities using Biot-Gassman theory and/or or porosity-velocity relationships. However, when using theory to relate surface seismic attributes to reservoir saturation, the porosity estimation problem and its affect on the unsaturated sedimentary properties should be addressed. Similarly, the problem of porosity estimation is also affected by the hydrocarbon saturation at the target. Because bulk modulus and density are affected by both porosity and saturation of the sediment, and because the shear modulus is affected by the sediment porosities, estimating hydrocarbon saturation and reservoir porosity from seismic data is a joint estimation problem. Moreover, the uncertainties associated with saturation estimation are related to the uncertainties we have in the porosity, and vice versa.
In this paper, we present a methodology for joint porosity-saturation estimation. We show how porosities and saturations can be jointly estimated using stochastic rock physics modeling and Bayesian estimation framework. We study the interdependencies of this reservoir property estimation problem within a given lithology unit.
Prediction of Three-Phase Relative Permeabilities Using a Pore-Scale Network Model Anchored to Two-Phase Data
Authors: Svirsky D. S. , van Dijke M. I. J. , Sorbie K. S. 
Published: December 2007

Abstract
A first attempt has been made to predict three-phase relative permeability experimental data of a water-wet Berea sandstone obtained by Oak1 using the three-phase flow network model for arbitrary wettability developed by van Dijke and Sorbie2. First, the network model is anchored to the corresponding two-phase relative permeability and capillary pressure data using an idealised representation of the pore geometry and a simple parameter fitting procedure. Then, predictions of three-phase properties are made, which are compared with experimental data as well as previous predictions from a different network models. The present study has confirmed that the relatively simple network model, anchored to experimental data, is able to predict three-phase relative permeabilities with reasonable accuracy, comparable to the accuracy using more complex models. Based on these preliminary results a limited sensitivity study is carried out with respect to different wettability states and two combinations of interfacial tensions. This study reveals some new results with respect to the invariance of relative permeability to interfacial tension combinations and the trend of water relative permeability as a function of the fraction of oil-wet pores in systems of non-uniform wettability.
Seismic Pore-pressure Imaging in Deepwater Offshore West Africa
Authors: Banik N. C. , Schultz G. , Wool G. , Bachrach R. , DenBoer L. , Mao W. , Huggard J. D. , Nibbelink K. , Taylor N. 
Published: December 2007

Abstract
Pore-pressure imaging in the frontier, deepwater offshore West Africa poses enormous challenges. The presence of large and deep radial canyons in the area makes conventional seismic velocity analysis methods inappropriate. Due to relatively younger and highly water-saturated deepwater sediments in the shallow sections, no compaction trend may be discernible for empirical methods for pore-pressure prediction. Moreover, because of the absence of deepwater wells, the calibration of mo del parameters for pore-pressure prediction is not possible.
Using automated, high-resolution, and spatially consistent 3D velocity analysis and a first-principle-based rock model, we have developed a methodology for pore-pressure imaging in the frontier, deepwater basins beset with complex geology. The rock model emphasizes burial history and shale diagenesis for pore-pressure generation. The methodology has been successfully applied to provide pre-drill prediction of pore pressure over a 600-km2 area (Figure 1) in deepwater offshore West Africa. A 2D seismic line tying a remote, shallow-water well greatly helped in developing the appropriate rock model.
The results, based on primarily seismic data and the associated geological interpretations, suggest a regressive pore-pressure system in the area. The onset of overpressure takes place at the seafloor. The pressure gradient increases slowly, but almost continuously, then rapidly to a maximum value, and then gradually reverses back to a lower value. The pressure profile closely follows certain geologic horizons and structures. Radial canyons on the seafloor significantly affect pore pressure. This paper describes these results in detail. Also presented in the paper are pore-pressure uncertainty estimation and a sensitivity analysis of the rock-model parameters for pore-pressure prediction.
Integrating Diverse Measurements to Predict Pore Pressure With Uncertainties While Drilling
Authors: Malinverno Alberto , Sayers Colin M. , Woodward Marta J. , Bartman Robert C. 
Published: December 2007

Abstract
An accurate estimate of the formation pore pressure is a key element for safe and cost-effective drilling. We describe here a methodology that integrates information from diverse measurements to predict pore pressure in a well before and during drilling. The data we can incorporate include surface seismics, sonic and density well logs, checkshots, mud weights, and measured pore pressures. The distinctive feature of our method is that uncertainties in the predicted velocities and pore pressures are quantified and updated while drilling. Quantifying uncertainties allows us to properly integrate all data and to show how measurements acquired while drilling reduce the uncertainty in the predicted pore pressures.
In practice, we use a Monte Carlo method to sample the posterior probability distribution (given all the data) of the compressional-wave velocity profile and of coefficients in the relationships that relate velocity to pore pressures. For each sampled value of velocities and other coefficients, we compute the corresponding pore-pressure profile along the well trajectory; the range of all sampled pore pressures describes the pore-pressure uncertainty. We demonstrate the methodology in two examples where the uncertainty in pore pressure predicted before drilling (from seismic velocities obtained by reflection tomography) is reduced by additional data acquired while drilling (checkshot, sonic log, mud weights, and pore-pressure data).
Taxonomy: A Knowledge Sharing Enabler
Authors: Smith David L. , Busch Joseph A. , Daniel Ron 
Published: December 2007

Abstract
As companies have begun to share information using intranets and internets, more information is available than the average person can absorb. A simple internet search using any search engine can return hundreds to thousands of documents that may contain only bits of information that one is required to integrate into useful knowledge. Successful implementation of internet and intranet portals, knowledge management, and content management requires a systematic approach to classifying information. The classification framework, or taxonomy, allows content owners to categorize their content so that it becomes more useful to a broader audience. Taxonomy allows knowledge seekers, whether they are researchers or the general public, to easily find and re-use information rather than recreate it.
Taxonomies are not new; in fact most work groups organize their information around a hierarchy that developed over several years. In most corporate environments, each product line or functional group organizes its information from its internal viewpoint rather than from a broader viewpoint. Use of a broader viewpoint would however, encourage content sharing beyond the product line or group. To be successful the digital world must eventually merge these independent hierarchies toward a common taxonomy.
This paper details the development of an enterprise-wide taxonomy for a diverse, global oil and gas services company. The paper describes implementation of the taxonomy, including the application of taxonomy to a comprehensive content management system, to knowledge management initiatives, and to effective portal design. Challenges and lessons learned are also presented, the most significant of which are issues surrounding the means to get people to think differently and do things in different ways.
Dynamic Flow-Based PEBI Grids for Reservoir Simulation
Authors: Mlacnik M. J. , Durlofsky L. J. , Heinemann Z. E. 
Published: December 2007

Abstract
A technique for the dynamic generation of flow based PEBI (perpendicular-bisectional) grids is presented and applied. The procedure includes a fine scale flow solution, the generation of an initial streamline–isopotential grid, grid optimization, and upscaling. The grid optimization is accomplished through application of a hybrid procedure with gradient and Laplace smoothing steps, while the upscaling is based on a global– local procedure that makes use of the global solution used in the grid determination step. The overall procedure is successfully applied to a complex channelized reservoir model involving changing well conditions. The gridding and upscaling procedures presented here may also be suitable for use with other types of structured or unstructured grid systems.
History Matching of Stochastic Models of Field-Scale Fractures: Methodology and Case Study
Authors: Jenni Sandra , Hu Lin Y. , Basquet Rémy , de Marsily Ghislain , Bourbiaux Bernard 
Published: December 2007

Abstract
This paper focuses on the history matching of stochastic models of large-scale (field-scale) fractures, namely (sub-) seismic faults and fracture swarms. First, we propose an object-based stochastic model for describing geological features of large-scale fractures. This model accounts for static constraints derived from seismic attributes, fault-related-strain-field, structural information (curvature) etc. Second, we review an upscaling procedure for performing fluid flow simulation in the presence of networks of large-scale fractures. Third, we present an algorithm for gradually moving and deforming stochastic fractures in the reservoir field while preserving their consistency with static constraints (location of seismic fractures, fracture density and orientation maps), whereby different realizations of the stochastic fracture network can be obtained. All these elements are integrated in an inverse procedure for calibrating stochastic models of large-scale fracture networks to hydrodynamic two-phase flow data.
The above methodology is applied to an actual fractured reservoir. We build a field-scale fracture network constrained to the fracture density map and the orientations of fracture sets. Then we perform history matching to water-cut data from four different zones of the field. Different calibration procedures are tested: global optimization that allows a general improvement of the model calibration to production data, and local calibration that further improves the match of each well. The results show the validity of the proposed methodology.
A Method to Diagnose Depletion, Skin,kh, and Drive Mechanism Effects Using Reservoir Monitoring Data
Authors: Haddad Sammy , Proano Eduardo , Patel Yogesh 
Published: December 2007

AbstractThe use of permanent sensors provides a continuous source of downhole pressure measurement throughout the life of the well. Moreover, multiphase flowmeters provide simultaneous flow rate measurements. The significant amount of data produced by these sensors has added a new dimension to traditional well test data interpretation techniques. This new dimension is life-of-well time. During the life of the well, permanent-gauge data and flow rate measurements are affected by several dynamic factors including changes in reservoir pressure, skin, permeability-thickness (kh), and reservoir drive mechanism. A major challenge facing the industry today is how to diagnose and respond to the effect of each of these factors. In other words, it is important to know whether a change in measured downhole pressure or flow rate is caused by depletion, changes in skin, kh, or drive mechanism.This study investigates the effects of the above-mentioned factors in two fields in the Gulf of Mexico area. In addition, experience and lessons learned from numerous wells and fields equipped with permanent sensors have been used to develop a diagnostic method. This method uses key reservoir and well performance indicators to derive techniques to distinguish between the effects of depletion, skin, kh and drive mechanism. Best practices in the collection of flow rate and pressure data with suggestions for near-future improvements are included.