Halliburton (United States)

Abstract We describe a new method for predicting well-log properties from seismic data. The analysis data consist of a series of target logs from wells which tie a 3-D seismic volume. The target logs theoretically may be of any type; however, the greatest success to date has been in predicting porosity logs. From the 3-D seismic volume a series of sample-based attributes is calculated. The objective is to derive a multiattribute transform, which is a linear or nonlinear transform between a subset of the attributes and the target log values. The selected subset is determined by a process of forward stepwise regression, which derives increasingly larger subsets of attributes. An extension of conventional crossplotting involves the use of a convolutional operator to resolve frequency differences between the target logs and the seismic data. In the linear mode, the transform consists of a series of weights derived by least-squares minimization. In the nonlinear mode, a neural network is trained, using the selected attributes as inputs. Two types of neural networks have been evaluated: the multilayer feedforward network (MLFN) and the probabilistic neural network (PNN). Because of its mathematical simplicity, the PNN appears to be the network of choice. To estimate the reliability of the derived multiattribute transform, crossvalidation is used. In this process, each well is systematically removed from the training set, and the transform is rederived from the remaining wells. The prediction error for the hidden well is then calculated. The validation error, which is the average error for all hidden wells, is used as a measure of the likely prediction error when the transform is applied to the seismic volume. The method is applied to two real data sets. In each case, we see a continuous improvement in predictive power as we progress from single-attribute regression to linear multiattribute prediction to neural network prediction. This improvement is evident not only on the training data but, more importantly, on the validation data. In addition, the neural network shows a significant improvement in resolution over that from linear regression.

Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. When a statistical characterization of the signal is available, Bayesian inference can complement conventional CS methods based on linear programming or greedy algorithms. We perform asymptotically optimal Bayesian inference using belief propagation (BP) decoding, which represents the CS encoding matrix as a graphical model. Fast computation is obtained by reducing the size of the graphical model with sparse encoding matrices. To decode a length-N signal containing <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> large coefficients, our CS-BP decoding algorithm uses O(K log(N)) measurements and O(N log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (N)) computation. Finally, although we focus on a two-state mixture Gaussian model, CS-BP is easily adapted to other signal models.

An experimental study of the migration of dilute suspensions of particles in Poiseuille flow at Reynolds numbers $\hbox{\it Re}\,{=}\,67\hbox{--}1700$ was performed, with a few experiments performed at $\hbox{\it Re}$ up to 2400. The particles used in the majority of the experiments were neutrally buoyant spheres with diameters $d$ yielding a ratio of pipe to particle diameter in the range $D/d \,{=}\, 8\hbox{--}42$ . The volume fraction of solids was less than 1% in all cases studied. The results of G. Segré &amp; A. Silberberg ( J. Fluid Mech. 14 , 136, 1962) have been extended to show that the tubular pinch effect in which particles accumulate on a narrow annulus is moved toward the wall as $\hbox{\it Re}$ increases. A careful comparison with asymptotic theory for Poiseuille flow in a channel was performed. Another inner annulus closer to the centre, and not predicted by this asymptotic theory, was observed at elevated $\hbox{\it Re}$ . As $\hbox{\it Re}$ is increased, the distribution of particles over the cross-section of the tube at the measurement location, lying at a distance $L \doteq 310 D$ from the entrance, changes from one centred at the annulus predicted by the theory to one with the particles primarily on the inner annulus. The case of slightly non-neutrally buoyant particles was also investigated. A particle trajectory simulation based on asymptotic theory was performed to facilitate the comparison of theory and the experimental observations.

Abstract This article presents a finite-volume scheme for multidimensional incompressible flows. Unstructured, solution-adaptive meshes composed of arbitrary convex palyhedra are used. A cell-centered equal-order formulation is developed. Gradients required for the evaluation of diffusion fluxes and for second-order-accurate convective operators are found by linear reconstruction. An additive-correction multigrid scheme is used to solve the resulting discrete equations. Pressure and velocity are stored at cell centers; momentum interpolation is used to prevent pressure checkerboarding. The SIMPLE algorithm is used for pressure-velocity coupling. Schemes for hanging-node and conformed adaption are implemented. The scheme is applied to benchmark problems using a variety of quadrilateral/hexahedral, triangular/tetrahedral, and hybrid meshes, and is shown to perform satisfactorily.

Abstract Along with horizontal drilling techniques, multi-stage hydraulic fracturing has improved shale gas production significantly in past decades. In order to understand the mechanism of hydraulic fracturing and improve treatment designs, it is critical to conduct modelling to predict stimulated fractures. In this paper, related physical processes in hydraulic fracturing are firstly discussed and their effects on hydraulic fracturing processes are analysed. Then historical and state of the art numerical models for hydraulic fracturing are reviewed, to highlight the pros and cons of different numerical methods. Next, commercially available software for hydraulic fracturing design are discussed and key features are summarised. Finally, we draw conclusions from the previous discussions in relation to physics, method and applications and provide recommendations for further research.

The first single-crystal diffraction studies on methane, propane, methane/propane, and adamantane gas hydrates SI, SII, and SH have been performed. To circumvent the problem of very slow crystal growth, a novel technique of in situ cocrystallization of gases and liquids resulting in oligocrystalline material in a capillary has been developed. With special data treatment, termed oligo diffractometry, structural data of the gas hydrates of methane, acetylene, propane, a propane/ethanol/methane-mixture and an adamantane/methane-mixture were obtained. Cell parameters are in accord with reported values. Host network and guest are subject to extensive disorder, reducing the reliability of structural information. It was found that most cages are fully occupied by a guest molecule with the exception of the dodecahedral cage in the acetylene hydrate which is only filled to 60%. For adamantane in the icosahedral cage a disordered model is proposed.

This paper unveils a thermal imaging methodology to recover the breathing waveform from the subject's nostrils. The resulting functionality is equivalent to that of a thermistor, but it is materialized in a contact-free manner. First, the nostril region is segmented and tracked over time through a network of cooperating probabilistic trackers. The mean thermal signal of the nostril region carries the breathing information. This information is extracted through wavelet analysis. The method has been tested on 20 healthy individuals. The breathing waveforms determined via the imaging computation were compared with the corresponding ones extracted from thermistors. The high degree of agreement between the two measurement methods confirms the validity of the proposed approach and opens the way for clinical applications. Furthermore, thermal imaging can be potentially used as an investigative tool to understand breathing physiology in ways not possible with contact sensors.

Abstract A 3-D finite-element solution has been used to solve controlled-source electromagnetic (EM) induction problems in heterogeneous electrically conducting media. The solution is based on a weak formulation of the governing Maxwell equations using Coulomb-gauged EM potentials. The resulting sparse system of linear algebraic equations is solved efficiently using the quasi-minimal residual method with simple Jacobi scaling as a preconditioner. The main aspects of this work include the implementation of a 3-D cylindrical mesh generator with high-quality local mesh refinement and a formulation in terms of secondary EM potentials that eliminates singularities introduced by the source. These new aspects provide quantitative induction-log interpretation for petroleum exploration applications. Examples are given for 1-D, 2-D, and 3-D problems, and favorable comparisons are presented against other, previously published multidimensional EM induction codes. The method is general and can also be adapted for controlled-source EM modeling in mining, groundwater, and environmental geophysics in addition to fundamental studies of EM induction in heterogeneous media.

The observation of inhomogeneous radial distributions of particles in tube flow dates from the work of Poiseuille (1836) who was mainly concerned by the flow of blood and the behavior of the red and white corpuscles it carries. These results were then generalized to non-biological flows and experiments on pipe flow of suspensions also indicated that significant deviations from ideal Poiseuille flow could occur in the presence of particles. We will consider systems where the fluid flow in the absence of particles is unidirectional. We will first present how fluid-particle interactions can induce lateral migration in the case of a single rigid particle in a shear flow, as a function of the Reynolds number. While the focus is upon inertial migration, a brief discussion of lateral migration in polymeric and viscoelastic fluids, where the nonlinearity results from the non-Newtonian behavior of the suspending fluid, will be presented at the conclusion of this Section. The role of interparticle interactions in a sheared fluid will be considered in the third section in the case of Stokes flow. The last section will briefly present how sedimentation can affect lateral motion.

Abstract Interpretation of seismic reflection data routinely involves powerful multiple-central-processing-unit computers, advanced visualization techniques, and generation of numerous seismic data types and attributes. Even with these technologies at the disposal of interpreters, there are additional techniques to derive even more useful information from our data. Over the last few years, there have been efforts to distill numerous seismic attributes into volumes that are easily evaluated for their geologic significance and improved seismic interpretation. Seismic attributes are any measurable property of seismic data. Commonly used categories of seismic attributes include instantaneous, geometric, amplitude accentuating, amplitude-variation with offset, spectral decomposition, and inversion. Principal component analysis (PCA), a linear quantitative technique, has proven to be an excellent approach for use in understanding which seismic attributes or combination of seismic attributes has interpretive significance. The PCA reduces a large set of seismic attributes to indicate variations in the data, which often relate to geologic features of interest. PCA, as a tool used in an interpretation workflow, can help to determine meaningful seismic attributes. In turn, these attributes are input to self-organizing-map (SOM) training. The SOM, a form of unsupervised neural networks, has proven to take many of these seismic attributes and produce meaningful and easily interpretable results. SOM analysis reveals the natural clustering and patterns in data and has been beneficial in defining stratigraphy, seismic facies, direct hydrocarbon indicator features, and aspects of shale plays, such as fault/fracture trends and sweet spots. With modern visualization capabilities and the application of 2D color maps, SOM routinely identifies meaningful geologic patterns. Recent work using SOM and PCA has revealed geologic features that were not previously identified or easily interpreted from the seismic data. The ultimate goal in this multiattribute analysis is to enable the geoscientist to produce a more accurate interpretation and reduce exploration and development risk.

This paper proposes the use of a risk measure based robust optimization bidding strategy for dispatching a wind farm in combination with energy storage. Through coordination with energy storage devices, variable wind generators can be utilized as dispatchable energy producers in the deregulated electricity market. The total profit from sale of electricity can be increased by exploiting arbitrage opportunities available due to the inter-temporal variation of electricity prices in the day ahead market. A case study is presented to show that as the forecast error in electricity price increases, the robust optimization based bidding strategy has an increasing probability of yielding better economic performance than a deterministic optimization based bidding strategy. The uncertainty set for robust optimization is selected based on the coherent risk measure conditional value at risk (CVaR). Uncertainties in electricity price forecasting and wind power forecasting are considered. The resulting robust optimization based bidding strategy is evaluated using Monte Carlo simulation for different choices of uncertainty sets.

This paper introduces a general framework for describing dynamic neural networks--the layered digital dynamic network (LDDN). This framework allows the development of two general algorithms for computing the gradients and Jacobians for these dynamic networks: backpropagation-through-time (BPTT) and real-time recurrent learning (RTRL). The structure of the LDDN framework enables an efficient implementation of both algorithms for arbitrary dynamic networks. This paper demonstrates that the BPTT algorithm is more efficient for gradient calculations, but the RTRL algorithm is more efficient for Jacobian calculations.

In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameters identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from concentration measurements in identifying unknown parameters. In this approach, the sampling locations that give the maximum expected relative entropy are selected as the optimal design. After the sampling locations are determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport equation. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. It is shown that the methods can be used to assist in both single sampling location and monitoring network design for contaminant source identifications in groundwater.

BACKGROUND: Transcriptome analysis was applied to characterize the physiological activities of Pseudomonas aeruginosa grown for three days in drip-flow biofilm reactors. Conventional applications of transcriptional profiling often compare two paired data sets that differ in a single experimentally controlled variable. In contrast this study obtained the transcriptome of a single biofilm state, ranked transcript signals to make the priorities of the population manifest, and compared rankings for a priori identified physiological marker genes between the biofilm and published data sets. RESULTS: Biofilms tolerated exposure to antibiotics, harbored steep oxygen concentration gradients, and exhibited stratified and heterogeneous spatial patterns of protein synthetic activity. Transcriptional profiling was performed and the signal intensity of each transcript was ranked to gain insight into the physiological state of the biofilm population. Similar rankings were obtained from data sets published in the GEO database http://www.ncbi.nlm.nih.gov/geo. By comparing the rank of genes selected as markers for particular physiological activities between the biofilm and comparator data sets, it was possible to infer qualitative features of the physiological state of the biofilm bacteria. These biofilms appeared, from their transcriptome, to be glucose nourished, iron replete, oxygen limited, and growing slowly or exhibiting stationary phase character. Genes associated with elaboration of type IV pili were strongly expressed in the biofilm. The biofilm population did not indicate oxidative stress, homoserine lactone mediated quorum sensing, or activation of efflux pumps. Using correlations with transcript ranks, the average specific growth rate of biofilm cells was estimated to be 0.08 h(-1). CONCLUSIONS: Collectively these data underscore the oxygen-limited, slow-growing nature of the biofilm population and are consistent with antimicrobial tolerance due to low metabolic activity.

Abstract An experimental and theoretical study is presented on spreading and retracting of a single drop impacting on a smooth surface at room temperature. The experimental study showed the influence of kinetic energy, liquid‐solid interaction, and energy dissipation on the impact process. The results are reported for Reynolds number from 180 to 5,513, Weber number from 0.2 to 176, four different liquids (distilled water, n‐Octane, n‐Tetradecane, and n‐Hexadecane), and four different surfaces (slide glass, uncoated silicon wafer, HMDS coated silicon wafer, and Teflon film). A theoretical model based on an energy balance was developed to predict the maximum spreading ratio at low impact velocity. The key novel feature of this model is that the shape of the drop is assumed to be a spherical cap during the spreading process. When compared to models in the literature, the present model not only gives better predictions for low drop impact velocities, but also in most cases gives predictions that are within 10% of the experimental data at high impact velocities.

Glucose, maltodextrin, and sucrose exhibit significant differences in their alkaline reaction properties and interactions in aluminate/silicate cement slurries that result in diverse hydration behaviors of cements. Using 1D solution- and solid-state (13)C nuclear magnetic resonance (NMR), the structures of these closely related saccharides are identified in aqueous cement slurry solutions and as adsorbed on inorganic oxide cement surfaces during the early stages of hydration. Solid-state 1D (29)Si and 2D (27)Al{(1)H} and (13)C{(1)H} NMR techniques, including the use of very high magnetic fields (18.8 T), allow the characterization of the hydrating silicate and aluminate surfaces, where interactions with adsorbed organic species influence hydration. These measurements establish the molecular features of the different saccharides that account for their different adsorption behaviors in hydrating cements. Specifically, sucrose is stable in alkaline cement slurries and exhibits selective adsorption at hydrating silicate surfaces but not at aluminate surfaces in cements. In contrast, glucose degrades into linear saccharinic or other carboxylic acids that adsorb relatively weakly and nonselectively on nonhydrated and hydrated cement particle surfaces. Maltodextrin exhibits intermediate reaction and sorption properties because of its oligomeric glucosidic structure that yields linear carboxylic acids and stable ring-containing degradation products that are similar to those of the glucose degradation products and sucrose, respectively. Such different reaction and adsorption behaviors provide insight into the factors responsible for the large differences in the rates at which aluminate and silicate cement species hydrate in the presence of otherwise closely related saccharides.

Numerical solutions of stationary flow resulting from immersion of a single body in simple shear flow are reported for a range of Reynolds numbers. Flows are computed using finite-element methods. Comparisons to results of asymptotic low-Reynolds-number theory, experimental study, and other numerical techniques are provided. Results are presented primarily for isotropic bodies, i.e. the circular cylinder and sphere, for both of which the two conditions of a torque-free (freely-rotating) and fixed body are investigated. Conditions studied for the sphere are $0 \,{&lt;}\, \hbox{\it Re} \,{\le}\, 100$ , and for the circular cylinder $0 \,{&lt;}\, \hbox{\it Re} \,{\le}\, 500$ , with the shear-flow Reynolds number defined as $\hbox{\it Re}\,{=}\, \gammadot a^2/\nu$ ; $\gammadot$ is the shear rate of the Cartesian simple shear flow $\bu \,{=}\, (\gammadot y, 0, 0)$ , $a$ is the cylinder or sphere radius, and $\nu$ is the kinematic viscosity of the fluid. In the torque-free case, the rotation rate of the body decreases with increasing $\hbox{\it Re}$ . Qualitative dependence, seen in the $\hbox{\it Re} \,{=}\,0$ fluid flow field, upon whether the body is fixed against rotation or torque-free vanishes as $\hbox{\it Re}$ increases and the fluid flow is more similar to that around the $\hbox{\it Re}\,{=}\,0$ fixed body: the influence of rotation of the body and the associated closed streamlines are confined to a narrow layer about the body for $\hbox{\it Re}\,{&gt;}\,O(1)$ . Separation of the boundary layer is observed in the case of a fixed cylinder at $\hbox{\it Re} \,{\approx}\, 85$ , and for a fixed sphere at $\hbox{\it Re} \,{\approx}\, 100$ ; similar separation phenomena are observed for a freely rotating cylinder. The surface stress and its symmetric first moment (the stresslet) are presented, with the latter providing information on the particle contribution to the mixture rheology at finite $\hbox{\it Re}$ . Stationary flow results are also presented for elliptical cylinders and oblate spheroids, with observation of zero-torque inclinations relative to the flow direction which depend upon the aspect ratio, confirming and extending prior findings.

ADVERTISEMENT RETURN TO ISSUEPREVCommunication to the...Communication to the EditorNEXTOpen Porous Polymer Foams via Inverse Emulsion Polymerization: Should the Definition of High Internal Phase (Ratio) Emulsions Be Extended?Angelika Menner, Ronald Powell, and Alexander BismarckView Author Information Department of Chemical Engineering, Polymer & Composite Engineering (PaCE) Group, Imperial College London, South Kensington Campus, London SW7 2AZ, UK, and Halliburton Energy Services, 2600 South 2nd Street, PO Box 1431, Duncan, Oklahoma 73536-0470 Cite this: Macromolecules 2006, 39, 6, 2034–2035Publication Date (Web):February 16, 2006Publication History Received19 December 2005Revised2 February 2006Published online16 February 2006Published inissue 1 March 2006https://doi.org/10.1021/ma052705xCopyright © 2006 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views1357Altmetric-Citations103LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (116 KB) Get e-AlertsSUBJECTS:Emulsions,Foams,Polymers,Porosity,Radical polymerization Get e-Alerts

Nanometer‐scale scanning electron microscopy was applied in visualizing the microscopic pores within shale kerogen. Geometrical information of all individual pores was extracted by image analysis. Image segmentation and separation showed that most of the intrakerogen pores are discrete and isolated from each other, having relatively spherical morphology. These isolated intrakerogen pores result in huge challenges in gas production, because they are not effectively connected to natural and hydraulic fractures. Statistical results showed that nanopores, which have diameters smaller than 100 nm, make up 92.7% of the total pore number, while they make up only 4.5% of the total pore volume. Intrakerogen porosity and specific surface area are 29.9% and 14.0 m 2 /g, respectively. Accurate visualization and measurement of intrakerogen pores are critical for evaluation of gas storage and optimization of hydraulic fracturing. By lattice Boltzmann simulations, permeabilities and tortuosities were simulated in the three principal directions. Long tails were observed in breakthrough curves, resulting from diffusion of solute particles from low‐flow‐velocity pores to larger conduits at late times. The long‐tailing phenomena at the pore scale are qualitatively consistent with those observed in real productions. Understanding the pore‐scale transport processes between microscopic pores within kerogen and large fracture systems is of great importance in predicting hydrocarbon production. Upscaling methods are needed to investigate larger‐scale processes and properties in shale reservoirs.

This paper investigates the profile of the copper losses in a 5.0-MW high-speed permanent-magnet machine with form-wound winding. The machine was tested, and the test data were used in conjunction with the results obtained from finite-element simulations to calculate the copper losses over the complete speed range. A retardation test in open-circuit conditions was performed to calculate the eddy current loss induced inside the stator coils by the rotor magnetic flux. The results demonstrate that this component of the copper losses is significantly high in form-wound machines at high-speed operation. The impact of the slot configuration on the proximity effect was also investigated. Open and semiclosed slots were compared, and the study results show that the high flux concentration in the slot opening region associated with semiclosed slots increases the proximity copper loss compared with the open-slot topology. At the end, this paper proposes the stator-alone test as a valid method to calculate resistance per phase at high frequency and estimates its increment over the direct current resistance.