**Current Filters**

**Source**

**Conference**

**Publisher**

**Theme**

**Author**

- Adams, Nicola (1)
- Ajo-Franklin, Jonathan B. (1)
- Al-Ajmi, Mohamed Dawaas (1)
- Al-Qadeeri, Bashar (1)
- Albertin, Martin L. (1)
- Aldana, Milagrosa (1)
- Amazonas, D. (1)
- Auken, Esben (1)
- Barnes, Gary (1)
- Barraud, Joseph (1)
- Baton, Penny (1)
- Beasley, Craig J. (1)
- Bloor, Robert (1)
- Bosch, Miguel (1)
- Burke, Nick (1)
- Calvert, Alexander (1)
- Carbonara, Stefano (1)
- Carvajal, Carla (1)
- Costa, J.C. (1)
- Dahl, Sverre Brandsberg (1)
- Daley, Thomas M. (1)
- Davies, Mark (1)
- DeAngelo, Mike (1)
- Doughty, Christine (1)
- Federici, Francesco (1)
- Fomel, Sergey (1)
- Glogovsky, Vladimir (1)
- Gomes, E.N.S. (1)
- Gomez, Carmen (1)
- Gonza´lez, Manuel (1)
- Gosselet, Adam (1)
- Hardage, Bob (1)
- Harris, Brett (1)
- Herrmann, Philippe (1)
- Houle, Patrick (1)
- Huang, Hai (1)
- Jefferson, Robert (1)
- Jenner, Edward (1)
- Jiao, Junru (1)
- Johnson, Timothy C. (1)
- Kamata, Masahiro (1)
- Keating, Pierre (1)
- Kepic, Anton (1)
- Lambaré, Gilles (1)
- Landa, Evgeny (1)
- Langman, Sergey (1)
- Le Moigne, José (1)
- Leaney, Scott (1)
- Leaney, W. Scott (1)
- Lemieux, Jean (1)
- Lin, Sonny (1)
- Lo´pez, Jaime J. Ri´os (1)
- Lovatini, A. (1)
- Lumley, John (1)
- Luthi, Stefan M. (1)
- Martínez, Jaime Ramos (1)
- Mavko, Gary (1)
- Melo, A. (1)
- Michaud, Gwénola (1)
- Moser, Tijmen Jan (1)
- Mukerji, Tapan (1)
- Murray, Paul (1)
- Narhari, Srinivasa Rao (1)
- Nutt, Les (1)
- Petmecky, R. Stephan (1)
- Ramkhelawan, Rosemarie (1)
- Roberts, Mark (1)
- Rodriguez, Juan (1)
- Rovetta, D. (1)
- Royle, Gillian (1)
- Rusic, Alberto (1)
- Sava, Diana (1)
- Schleicher, J. (1)
- Sears, Tim (1)
- Sen, Mrinal (1)
- Sierra, Jesús (1)
- Sil, Samik (1)
- Silva, F.J.C. (1)
- Singh, Satish (1)
- Smith, Richard S. (1)
- Srinivasan, Sanjay (1)
- St.Clair, Chris (1)
- Søndergaard, Verner H. (1)
- Tanoli, Saifullah Khan (1)
- Tetyukhina, Daria (1)
- Torres, Astrid (1)
- Touré, Jean Paul (1)
- Underhill, William (1)
- Urosevic, Milovan (1)
- Vallée, Marc A. (1)
- van Vliet, Lucas J. (1)
- Versteeg, Roelof J. (1)
- Vidal, Madain Moreno (1)
- Wapenaar, Kees (1)
- Watts, M.D. (1)
- Yang, Peijie (1)
- Yin, Xingyao (1)
- Zhang, Guangzhi (1)
- Zhang, Ian (1)
- Zhou, Chaoguang (1)
- Zimine, Serge (1)

to

Go **Concept Tag**

- acquisition (2)
- Albertin (1)
- algorithm (3)
- anisotropy (2)
- anomaly (2)
- application (2)
- approach (4)
- aquifer (2)
- Archie Equation (1)
- Artificial Intelligence (9)
- AVO inversion (1)
- Baker Hughes (1)
- Bayesian (1)
- Bayesian Inference (1)
- Beenyup (1)
- Billette (1)
- Brine (1)
- Business Impact (1)
- Cassm (1)
- CDP (1)
- change (2)
- Chibougamau (1)
- CIG (1)
- Claerbout (1)
- constraint (3)
- Crosswell CASSM (1)
- CSEM (1)
- Denmark (1)
- Depositional Trend (1)
- difference (2)
- direction (2)
- Directional Resistivity Tool (1)
- distribution (5)
- electrical resistivity (2)
- Engineer (1)
- equation (2)
- estimate (2)
- estimation (3)
- Exhibition (1)
- field (2)
- Fluid Dynamics (1)
- Fomel (1)
- formation evaluation (5)
- Forward Model (1)
- frequency (2)
- function (3)
- GAC (1)
- GAC sensor (1)
- geologic modeling (2)
- geological modeling (2)
- geophysics (5)
- gradient (2)
- Gradiometer (1)
- horizon (2)
- Horizontal (1)
- Hydrogeology (1)
- image (2)
- Imaging (1)
- Indicator (1)
- Industry (1)
**information (27)**- injection (2)
- inversion (10)
- Inversion Algorithm (1)
- joint inversion (3)
- Kuwait (1)
- layer (2)
- location (4)
- log (2)
- log analysis (3)
- machine learning (4)
- method (5)
- migration (2)
- model (11)
- modeling (2)
- posterior (3)
- prestack (3)
- PreSTM (2)
- probability (2)
- problem (3)
- property (2)
- receiver (2)
- reflection (3)
- regularization (2)
- Reservoir Characterization (26)
- reservoir description and dynamics (27)
- resistivity (4)
- seg las vegas (10)
- seismic processing and interpretation (22)
- signal (3)
- solution (3)
- source (4)
- survey (5)
- technique (2)
- tomography (2)
- Trend (2)
- Upstream Oil & Gas (25)
- waveform (2)
- well (8)
- well logging (3)

to

GoAn inversion procedure is described wherein microseismic data recorded by a network of three component geophones are assumed to be represented as the sum of a compressional (P) and one or two shear (S) arrivals. The inversion operates in the frequency-space domain and includes a linear inversion for source waveforms and a nonlinear inversion for model properties or source locations. The linear inversion effectively reverses time using a ray trace Green function to recover the source-time functions. For the nonlinear inversion two waveform fitting functionals are constructed; one captures moveout and polarization information through a reconstructed data misfit, another captures information from arrival time differences through a spectral coherence functional. The two may be scaled and summed to form a joint X2 misfit which may be combined with soft prior information in a Bayesian posterior. This is then maximized using global search techniques. Model calibration is accomplished by inverting waveform data from known locations (e.g. perforation shots) for anisotropy and optionally for model smoothness and Q. Micro-earthquake event locations are determined by inverting waveform data given the calibrated model.

Since the procedure involves fitting waveforms, time picking is not required. The beam-forming property of the receiver array and the complete polarization vector are used to enhance the signal to noise ratio of arrivals. The presence of a P arrival is not necessary to determine a location. The algorithm implementation uses layered VTI models, includes losses due to spreading, transmission and Q and handles an arbitrary distribution of receivers (e.g. from horizontal or multiple wells or surface locations). The inversion permits automated, objective data analysis with quantified uncertainties in estimated unknowns.

amplitude, anisotropy, approach, arrival, Artificial Intelligence, frequency, function, information, inversion, least-squares time reversal, location, model, objective function, posterior, receiver, Reservoir Characterization, reservoir description and dynamics, seismic processing and interpretation, source, source function, source location, Upstream Oil & Gas, waveform, waveform fitting

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Technology: Information Technology > Artificial Intelligence > Representation & Reasoning > Search (0.34)

Zhou, Chaoguang (Petroleum Geo- Services) | Martínez, Jaime Ramos (Petroleum Geo- Services) | Lin, Sonny (Petroleum Geo- Services) | Jiao, Junru (Petroleum Geo- Services) | Dahl, Sverre Brandsberg (Petroleum Geo- Services)

Tomography has been widely employed for velocity model building. The typical work flow starts with the migration of an initial velocity model. This is followed by picking residual moveouts and then updating the velocity through tomography. The migration process provides common image gathers and a stack. At the tomography stage, a ray tracer is used to trace specular rays from the image points to the surface to set up the system of linear equations for the tomographic inversion by linking valid ray pairs to their corresponding residuals. For narrow azimuth (NAZ) surveys, searching for valid ray pairs is usually limited to a narrow azimuth band. Since neither the gathers nor the stack contain acquisition geometry information, the selected specular ray pairs may not reflect the true ray paths, resulting in inaccurate rays being used in the inversion process. In addition to the problem of "which rays to choose", we also have the problem of "how many rays to choose". These problems are even more difficult to handle with acquisition configurations other than NAZ, such as the wide azimuth towed-streamer (WATS) surveys, multi-azimuth (MAZ) surveys, and ocean bottom cable (OBC) surveys. To overcome these problems, we propose a tomography method that incorporates the acquisition geometry information and uses vector offsets to account for both offsets and azimuths. To address the ill-posed nature of the system of equations, we developed an anisotropic Laplacian regularization operator that allows different smoothing along different directions. We validate the method with tests on both synthetic and field data with a WATS geometry.

acquisition, acquisition geometry information, anisotropic regularization, building, direction, gather, geometry, information, migration, model, pair, ray, regularization, Reservoir Characterization, reservoir description and dynamics, residual moveout, seismic processing and interpretation, specular, survey, tomography, true geometry tomography, Upstream Oil & Gas

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Seismic imaging in depth is limited by the accuracy of velocity model estimation. Slope tomography uses the slowness components and traveltimes of picked reflection or diffraction events for velocity model building. The unavoidable data incompleteness requires additional information to assure stability to inversion. One natural constraint for ray based tomography is a smooth velocity model. We propose a new, reflectionangle- based kind of smoothness constraint as regularization in slope tomography and compare its effects to three other, more conventional constraints. The effect of these constraints are evaluated through comparison of the inverted velocity models as well as the corresponding migrated images. We find the smoothness constraints to have a distinct effect on the velocity model but a weaker effect on the migrated data. In numerical tests on synthetic data, the new constraint leads to geologically more consistent models.

Slope tomography is one of the many methods that try to determine a macrovelocity model for time or depth imaging. It uses slowness vector components to improve and stabilize the traveltime inversion. Slope tomography was initially proposed by Billette and Lambaré (1998) as a robust tomographic method for estimating velocity macro models from seismic reflection data. They had recognized the potential efficiency of traveltime tomography (Bishop et al., 1985; Farra and Madariaga, 1988) but also the difficulties associated with a highly interpretative picking. The selected events have to be tracked over a large extent of the pre-stack data cube, which is quite difficult for noisy or complex data. The idea is to use locally coherent events characterized by their slopes in the pre-stack data volume. Such events can be interpreted as pairs of ray segments and provide independent information about the velocity model.

However, the data for slope tomography are incomplete (Bishop et al., 1985). This causes depth and velocity ambiguities that depend strongly on the size of the acquisition aperture (Bube et al., 2005). Therefore, stability and convergence can only be achieved if additional information is prescribed. This additional information contains desirable properties for the solution, reducing ambiguity (Menke, 1989). It can be shown that stability is obtained only if we try to determine a smooth model of the subsurface (Delprat-Jannaud and Lailly, 1992, 1993). Moreover, for ray based inversion, smoothness is a requirement, because rough models cause the forward problem to break down during linear iterations. The use of combined smoothness constraints enables an interpretation-oriented inversion while keeping solutions consistent with the data.

We investigate the effect of different kinds of smoothness constraints in slope tomography, prescribing lateral, vertical and isotropic smoothing constraints in different combinations. Moreover, we propose a structurally motivated smoothing constraint in the direction of a potential reflector. This regularization is based on information that is contained in the data, in contrast to standard regularizations that impose global smoothness constraints. We test the different regularizations on the Marmousoft data set (Billette et al., 2003).

Slope tomography differs from conventional reflection tomography by the data that are used for the inversion (Billette et al., 2003).

Billette, constraint, coverage, difference, direction, gradient, image, information, inversion, model, reflection, reflector, regularization, Reservoir Characterization, reservoir description and dynamics, seg las vegas, seismic processing and interpretation, slope tomography, smoothness constraint, tomography, traveltime, Upstream Oil & Gas

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Time-lapse electrical resistivity data has been recognized as a source of information for estimating reservoir flow properties in environmental applications. However, transient resistivity data have not been fully utilized in such estimations due to 1) the general lack of an adequate petrophysical transform, and 2) the limited resolution of electrical resistivity tomograms. In this paper we discuss the current limitations in using time-lapse resistivity data to constrain inverse estimates of hydraulic conductivity and present a method that addresses each limitation by leveraging the strong correlation between changes in fluid and bulk conductivity. We demonstrate with a synthetic example the rich amount of information provided by resistivity data and show how that information can be extracted in a meaningful and unbiased manner to estimate reservoir flow properties using a data domain correlation approach.

approach, bulk conductivity, change, correlation, distribution, electrical resistivity, estimate, fluid conductivity, formation evaluation, hydraulic conductivity, information, inverse, inversion, joint inversion, log analysis, property, proxy resistivity, Reservoir Characterization, reservoir description and dynamics, resistivity, solution, Upstream Oil & Gas, US government, well logging

Industry:

- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > North America Government > US Government (0.32)

SPE Disciplines:

Structural information is the most important content of seismic images. I introduce a numerical algorithm for spreading information in 3-D volumes according to the local structure of seismic events. The algorithm consists of two steps. First, local, spatially-variable slopes of seismic events are estimated (inline and crossline in 3-D) by the plane-wave-destruction method. Next, a seed trace is inserted in the volume, and the information contained in that trace is spread inside the volume, thus automatically "painting" the data space. Immediate applications this technique include automatic horizon picking and flattening in applications to both prestack and post-stack seismic data. Synthetic and field data tests demonstrate the effectiveness of predictive painting.

Structural information is the most important content of seismic images. One way to characterize structure is to assign a dominant local slope attribute to all elements in a volume. Claerbout (1992) proposed the method of plane-wave destruction for detecting local slopes of seismic events. Closely related ideas were developed in the differential semblance optimization framework (Symes, 1994; Kim and Symes, 1998). Planewave destruction finds many important applications in seismic data analysis, including data regularization, noise attenuation, etc. (Fomel, 2002, 2007; Fomel et al., 2007).

The main principle of plane-wave destruction is prediction: each seismic trace gets predicted from its neighbors that are shifted along the event slopes, and the prediction error gets minimized to estimate optimal slopes. In this paper, I propose to extract the prediction operator from the plane-wave destruction process and to use it for recursive spreading of information inside the volume. I call this spreading predictive painting. One particular kind of information that becomes meaningful when spread in a volume is relative geologic age, in the terminology of Stark (2004): seismic layers arranged according to the relative age of sedimentation. Once relative geological age is established everywhere in the volume, it is possible to flatten seismic images by extracting stratal slices (Zeng et al., 1998a) without manual picking of horizons. Even though flattened seismic horizons do not necessarily correspond to equivalent true geologic ages, flattening improves the interpreter’s ability to understand and quantify the structural architecture of sedimentary layers (Zeng et al., 1998b). The idea of using local shifts for automatic picking was introduced by Bienati and Spagnolini (2001) and Lomask et al. (2006). Stark (2003) presented an alternative approach involving instantaneous phase unwrapping. The predictive painting method, introduced in this paper, provides yet another alternative, with superior computational performance.

Plane-wave destruction (Fomel, 2002) originates from a local plane-wave model for characterizing seismic data. The mathematical basis is the local plane differential equation. Prediction of a trace from a distant neighbor can be accomplished by simple recursion. .I call this recursive operator predictive painting. Once the elementary prediction operators in equation (4) are determined by plane-wave destruction, predictive painting can spread information from a given trace to its neighbors recursively by following seismic structure. The next section illustrates the painting concept using 2-D examples.

algorithm, Claerbout, crossline, equation, Fomel, geophysics, horizon, information, operator, plane-wave destruction, prediction, prediction operator, predictive painting, prestack, Reservoir Characterization, reservoir description and dynamics, seg las vegas, seismic processing and interpretation, slope, trace, Upstream Oil & Gas

Hydrocarbon production from the Minagish Formation of early Cretaceous Berriasian - Tithonian age in Kuwait is primarily from the West Kuwait fields such as Minagish and Umm Gudair. Exploratory efforts for this play equivalent have not met with much success in other parts of the country. Primary reason for this is lack of trap integrity in the west and unfavorable facies development in the north and north-east.

Integration of seismic analysis and geological understanding was applied to decipher the palaeo-basin configuration for this play in western Kuwait. Analysis of seismic attributes, particularly seismic waveform characterization has helped in mapping an edge of the prograding, high energy facies corresponding to the middle unit of the Minagish Formation, which is the main producing unit in the West Kuwait fields. Both 2D and 3D seismic data sets were used for this study. 2D data has provided the general trend of the facies, whereas with the addition of 3D seismic it was possible to delineate the approximate edge of the facies boundary.

The Minagish Formation is part of the Berriasian - Tithonian age, Lower Cretaceous section (Fig 2a Generalized stratigraphic column of Kuwait)). This is a period of rifting encompassing a return to open marine deposition from intra-shelf conditions prevailing during the late Jurassic period. The Berriasian interval is mostly void of major structural activity in Kuwait. It includes the flooding event of the Gotnia basin followed by deposition of Oolite/grainstone around its margins and subsequent re- flooding of the basin.

The Minagish Formation in Kuwait can broadly be divided into three units (Fig 2b Type log of Minagish Formation) consisting of (A) lower transgressive argillaceous limestone overlain by (B) the middle progradational unit comprising relatively high energy (oolitic) carbonates. This oolitic zone represents good reservoir facies and is the main producer in the West Kuwait oil fields. The middle unit is in turn overlain by (C) another transgressive unit consisting of argillaceous carbonates of poor reservoir quality.

analysis, case study, depositional, Depositional Trend, facies, formation, information, integration, Kuwait, Minagish, Minagish Field, Minagish Formation, Reservoir Characterization, reservoir description and dynamics, seismic facies, seismic processing and interpretation, study, Trend, Upstream Oil & Gas, waveform characterization, well

Geologic Time:

- Phanerozoic > Mesozoic > Jurassic > Upper Jurassic (0.89)
- Phanerozoic > Mesozoic > Cretaceous > Lower Cretaceous (0.88)

Oilfield Places:

- Asia > Middle East > Kuwait > West Kuwait > Minagish Oil Field (0.99)
- Asia > Indonesia > East Kalimantan Offshore > Mahakam Block > Peciko Gas Field (0.99)
- Asia > Middle East > Kuwait > West Kuwait > Umm Gudair Field (0.94)

Modern hydrogeology is increasingly depending numerical modeling to simulate impacts of water resource projects tens or hundreds of years into the future. The validity of such numerical models is highly dependent on accurate distribution of key hydrogeological parameters. A combination of 3D seismic reflection and Vertical Seismic Profiling (VSP) offers the potential to create detailed, accurate, hydrostratigraphic frameworks within which physical, chemical and biological properties can be distributed. We provide the outline and outcomes from a systematic research program designed to develop and optimize 3D seismic reflection and VSP techniques tailored for hydrogeology. The test site is located at the Beenyup Waste Water Treatment Plant near a major freeway, where a long term high volume purified waste water injection trial will commence in 2009.

application, aquifer, Beenyup, design, geophone string, Hydrogeology, hydrophone, hydrophone string, information, injection, program, purified waste water, recovery, Reservoir Characterization, reservoir description and dynamics, seismic processing and interpretation, seismic reflection, string, surface, Upstream Oil & Gas, VSP, water management, well

Industry:

- Water & Waste Management > Water Management > Lifecycle > Treatment (1.00)
- Energy > Oil & Gas > Upstream (1.00)

Prestack AVO inversion is multidimensional and ill posed, and is often strongly affected by noise and measurement uncertainty, so it is necessary to performing constraints in order to obtain steady and rational inversion results. A novel prestack AVO inversion method is developed based on linearized approximation of the Zoeppritz equation and Bayesian parameter estimation theory. Covariance matrix is used to describe the correlation degree between the parameters, Gaussian distribution is used for likelihood function and modified Cauchy distribution is used for prior distribution; P-wave impedance, S-wave impedance, density and rock physics relation are applied to constrain the inversion results, thus it is reliable to make the result more accurate and steady. Tests on synthetic and real data show that all inverted parameters are almost perfectly retrieved even the SNR is low.

Artificial Intelligence, AVO inversion, Bayesian Inference, CDP, cdp cdp cdp, constraint, distribution, equation, estimation, function, information, inversion, likelihood function, machine learning, Novel Prestack AVO Inversion, posterior, prestack, reflectivity, Reservoir Characterization, reservoir description and dynamics, s-wave impedance, seismic processing and interpretation, Upstream Oil & Gas

Technology:

Lo´pez, Jaime J. Ri´os (Pemex PEP Marina Noreste) | Vidal, Madain Moreno (Pemex PEP Marina Noreste) | Gonza´lez, Manuel (G&W Systems Corp.) | Rusic, Alberto (G&W Systems Corp.) | Srinivasan, Sanjay (University of Texas) | Sen, Mrinal (University of Texas) | Sil, Samik (University of Texas)

Reservoir models are built using disparate datasets each of which may be prone to experimental and interpretational errors and therefore a resulting reservoir model is generally associated with uncertainties. One of the primary sources of uncertainties lies in the structure (or reservoir architecture) estimation from seismic data. Geostatistics can be used to integrate seismic data with well data for the purpose of structural uncertainty estimation. In this paper we present a case study from the Gulf of Mexico, where structural uncertainty associated with a seismic horizon is modeled using Markov-Bayes stochastic simulation. For this simulation, seismic data is used as "soft" or secondary data while well log derived marker depths are used as hard data. Simulation results show uncertainty distributions with smaller variance in the vicinity of the wells. However, in regions away from the wells, the interpreter-picked horizon appears to fall outside the error bounds predicted by our stochastic algorithm. Lack of well control, existence of faults, improper choice of seismic processing parameters (error in time migrated images) and interpreters’ bias are some of the plausible causes of this disparity.

Artificial Intelligence, covariance, cross covariance, distribution, estimation, geologic modeling, geological modeling, hard data location, horizon, Indicator, information, interpolation, location, machine learning, Markov, Reservoir Characterization, reservoir description and dynamics, reservoir model, scattergram, seg las vegas, seismic processing and interpretation, Simulation, structural uncertainty estimation, threshold, Upstream Oil & Gas, well

SPE Disciplines:

Technology:

Integrated service companies have recently developed multi-component induction wireline and LWD resistivity logging tools. The new wireline induction logging tools are 3DEX from Baker Hughes and Rt Scanner from Schlumberger. The directional LWD resistivity tools include azimuthal propagation resistivity (APR) from Baker Hughes, azimuthal deep resistivity (ADR) by Halliburton, and PeriScope15 and PeriScope100 from Schlumberger. These new tools have multiple or 3-component transmitters and receivers, and thus can provide information about directionality and anisotropy. This information has led to some important applications. The 3DEX and Rt Scanner have been used to detect resistivity anisotropy of the formation, which allows a better quantification of hydrocarbon-in-place and hydrocarbon recovery.

SPE Disciplines: Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)

Thank you!