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GoWe propose a robust interpolation scheme for aliased regularly sampled seismic data that uses the curvelet transform. In a first pass, the curvelet transform is used to compute the curvelet coefficients of the aliased seismic data. The aforementioned coefficients are divided into two groups of scales: alias-free and alias-contaminated scales. The alias-free curvelet coefficients are upscaled to estimate a mask function that is used to constrain the inversion of the alias-contaminated scale coefficients. The mask function is incorporated into the inversion via a minimum norm least squares algorithm that determines the curvelet coefficients of the desired alias-free data. Once the alias-free coefficients are determined, the curvelet synthesis operator is used to reconstruct seismograms at new spatial positions. Synthetic and real data examples are used to illustrate the performance of the proposed curvelet interpolation method.

INTRODUCTION

Interpolation and reconstruction of seismic data has become an important topic for the seismic data processing community. It is often the case that logistic and economic constraints dictate the spatial sampling of seismic surveys. Wave-fields are continuous; in other words, seismic energy reaches the surface of the earth everywhere in our area of study. The process of acquisition records a finite number of spatial samples of the continuous wave field generated by a finite number of sources. This leads to a regular or irregular distribution of sources and receivers. Many important techniques for removing coherent noise and imaging the earth interior have stringent sampling requirements which are often not met in real surveys. In order to avoid information losses, the data should be sampled according to the Nyquist criterion (Vermeer, 1990). When this criterion is not honored, reconstruction can be used to recover the data to a denser distribution of sources and receivers and mimic a properly sampled survey (Liu, 2004). Methods for seismic wave field reconstruction can be classified into two categories: wave-equation based methods and signal processing methods. Wave-equation methods utilize the physics of wave propagation to reconstruct seismic volumes. In general, the idea can be summarized as follows. An operator is used to map seismic wave fields to a physical domain. Then, the modeled physical domain is transformed back to data space to obtain the data we would have acquired with an ideal experiment. It is basically a regression approach where the regressors are built based on wave equation principles (in general, approximations to kinematic ray theoretical solutions of the wave equation). The methods proposed by Ronen (1987), Bagaini and Spagnolini (1999), Stolt (2002), Trad (2003), Fomel (2003), Malcolm et al. (2005), Clapp (2006) and Leggott et al. (2007) fall under this category. These methods require the knowledge of some sort of velocity distribution in the earth’s interior (migration velocities, root-meansquare velocities, stacking velocities). While reconstruction methods based on wave equation principles are very important, this paper will not investigate this category of reconstruction algorithms. Seismic data reconstruction via signal processing approaches is an ongoing research topic in exploration seismology.

algorithm, Artificial Intelligence, coefficient, conjugate gradient, curvelet, curvelet coefficient, Curvelet transform, direction, domain, function, geophysics, interpolation, machine learning, mask function, method, operator, reconstruction, representation, Reservoir Characterization, reservoir description and dynamics, scale, seismic processing and interpretation, Upstream Oil & Gas

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

Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.48)

Acoustic finite-difference modeling is playing an increasingly important role in seismic imaging (e.g. in reverse time migration) but the additional cost of elastic finite-difference modeling restricts its use in commercial imaging technology. The cost of full elastic finite-difference modeling can exceed the cost of acoustic modeling in the same velocity model by two orders of magnitude or more. A technique is described that corrects an acoustic finite-difference simulation for elastic effects. It is based on calculating the errors in the elastic wave equation using the acoustic simulation as an approximate solution. The errors are used to generate an effective source field for an additional acoustic simulation that calculates a correction to the wavefield produced in the original acoustic simulation. The cost of this approach is greater than that of an acoustic simulation but much less than that of a full elastic simulation.

For many applications in imaging and reservoir characterization (reverse time migration, waveform inversion, etc.), we require accurate simulations of seismic wave propagation. To realistically model the Earth, these are needed for elastic, anisotropic and anelastic models. The finite-difference method is widely used in this context as it is robust, simple to implement, and offers a good balance between accuracy and efficiency. However, it is still a computational challenge to perform elastic, anisotropic finite-difference simulations in three dimensions, so approximate calculations are often performed in an equivalent acoustic model. Even for P waves, the amplitudes of the first arrivals in the acoustic medium differ from those in the elastic medium. The objective of this paper is to describe a scheme whereby the acoustic wavefield can be partially corrected for elastic effects without incurring the cost of the full elastic computation. Consider two models, one acoustic and the other elastic, designed so that the density and acoustic/P-wave velocity fields match. For a pressure source, only P waves will be excited, so the solutions in the acoustic medium and for P waves in the elastic medium are expected to be very similar, at least in a limited time window around the first arrivals. The most significant differences will occur in the amplitudes of reflected and transmitted P waves from interfaces (or pseudointerfaces where properties vary rapidly). In the regions away from interfaces, properties are either homogeneous or varying slowly and smoothly and the coupling between P and S waves is insignificant. The objective is to correct the acoustic solution for elastic effects at interfaces, without incurring the cost of the full elastic solution. This paper describes a method to correct acoustic simulations for some of the effects of elasticity. We hope to correct the amplitudes of the P-wave arrivals for the effects of elasticity, particularly those caused by reflection and transmission coefficients at interfaces, at a cost considerably less than the cost of full elastic simulations. We do not expect to simulate the shear waves generated at interfaces. If necessary, the process can be applied iteratively to improve the accuracy of the correction.

acoustic equation, acoustic finite-difference simulation, acoustic simulation, acoustic solution, amplitude, elastic correction, elastic effect, elastic equation, elastic finite-difference correction, equation, error, interface, method, model, particle, Reservoir Characterization, reservoir description and dynamics, seismic processing and interpretation, solution, source, Upstream Oil & Gas, Wave

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

amplitude, background, background model, change, chemical flooding methods, CO2 capture, CSEM, difference, enhanced recovery, formation evaluation, Miller, model, model response, Reservoir Characterization, reservoir description and dynamics, resistivity, Response, saturation, Scenario, site, Sleipner, Storage Site, subsurface storage, target model, Upstream Oil & Gas

Oilfield Places:

- Europe > United Kingdom > North Sea > Central North Sea > Miller Field (0.99)
- Europe > Norway > North Sea > Central North Sea > Sleipner Gas and Condensate Field (0.99)

SPE Disciplines:

algorithm, Artificial Intelligence, correlation, crossline, data-independent multicomponent interpolator, dimensional, interpolation, Interpolation Error, interpolator, interpolator coefficient, optimal, optimal reconstruction, particle, reconstruction, Reservoir Characterization, reservoir description and dynamics, seismic processing and interpretation, separation, spectrum, streamer, Upstream Oil & Gas, vector, wavefield

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

INTRODUCTION

The information that seismic processing aims to extract from the data is: (1) an estimate of the structural map of the earth, and (2) estimates about the mechanical properties of the target (possible hydrocarbon reservoirs). The process of estimating the earth’s material properties (porosity, velocity, density, etc.) is called inversion. Ultimately, this information is interpreted to deduce the geological structure, size, and type of possible hydrocarbon accumulations. In this abstract, we focus on methods to estimate the earth’s subsurface model given the best fit to the recorded data in the sense of minimizing the data misfit using a specific metric (e.g., the L2 norm). Most of these methods use iterative schemes, in which the model is updated based on a search direction computed from a gradient of a cost function. These types of optimization problems have been given a lot of attention in seismic exploration in recent years; one example is full waveform inversion (FWI). The FWI theory was originally developed by Tarantola (1984, 1988); its most general formulation involves a quadratic objective function measuring the differences (in terms of dynamics and kinematics) between model data and measured data. The inverted model, which generates a realization of model data that minimizes the objective function, is the output of FWI. The goal of FWI is to invert for a model that closely describes the actual model (earth) that produced the measured data (Crase et al., 1990; Ikelle et al., 1986; Sirgue et al., 2008; Vigh and Starr, 2008). The final solution to a geophysical inverse problem like FWI might differ significantly from any ideal solution due to data incompleteness, errors in the model parameterization, violation of assumptions (e.g., assuming an acoustic model to invert elastic measured data), noise, intrinsic issues with the specific mathematical description of the problem (e.g., under-determined problems), etc. Seismic inverse problems are, in general, ill-posed and ill-conditioned; thus, their null space can be significantly large and many different solutions (models) can exist that fit a given dataset equally well.

Technology: Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.73)

Zhu, Xianhuai (ConocoPhillips) | Wallace, Kirk (ConocoPhillips) | Anno, Phil (ConocoPhillips) | Zhu, Qingrong (ConocoPhillips) | Day, Richard (ConocoPhillips) | Ma, Nan (ConocoPhillips) | Hartline, Craig (ConocoPhillips) | Shen, Yunqing (ConocoPhillips) | Hofer, Robert (ConocoPhillips)

gas-induced scatterer, Imaging, Imaging Challenge, line, migration, model, Prestack Depth Migration, prestack depth migration image, Reservoir Characterization, reservoir description and dynamics, SEG SEG Denver, seismic processing and interpretation, shallow scatterer, structure, test line, turning-ray tomography, Upstream Oil & Gas, well

Tuanyu, Teng (Research Institute of Petroleum Exploration & Development-Northwest(NWGI), PetroChina) | Huquan, Zhang (Research Institute of Petroleum Exploration & Development-Northwest(NWGI), PetroChina) | Hongbin, Wang (Research Institute of Petroleum Exploration & Development-Northwest(NWGI), PetroChina) | Haifeng, Cui (Research Institute of Petroleum Exploration & Development-Northwest(NWGI), PetroChina)

2．The technique of impendence change rate to predict fractured reservoir During carbonate reservoir research, a comprehensive and accurate understanding can be got through analyzing the geological factors of controlling reservoir development, which includes internal and external factors: Internal factors namely primary texture and physico-chemical properties of rocks, mainly determined by the sedimentary environment. The impacts of internal factors are often large-scale and relatively homogeneous, so they contribute little to the reservoir heterogeneity. External factors include physical force and chemical force, corresponding to the tectonic stress action and the weathering dissolution. The two roles can enhance the heterogeneity of the reservoir simultaneously and jointly [1]. Dense carbonate rocks have characteristics of high density and high speed. Whether internal or external roles act, its speed and density will be reduced, and impedance will be reduced accordingly. This change will reduce the impedance variance between reservoir and surrounding rocks. Impedance variance is the comprehensive effect of strata velocity and density.

azimuth, Cambrian, carbonate fractured, change, distribution, dolomite, exploration, fracture, fractured reservoir, Hill, impedance, impendence, impendence change, prediction, prediction method, research, reservoir, Reservoir Characterization, reservoir description and dynamics, seismic processing and interpretation, surface, Upstream Oil & Gas, well

Oilfield Places:

- Asia > China > Xinjiang Uyghur Autonomous Region > Tarim Basin (0.99)
- Asia > China > Xinjiang Province > Tahe Oil Field (0.97)

SPE Disciplines:

approximation, assumption, change, compressibility, conclusion, difference, Eqn, fluid dependence, Gassmann, geophysics, HART, incompressibility, pore, Reservoir Characterization, reservoir description and dynamics, rock, rock compressibility, SEG, SEG SEG Denver, seismic processing and interpretation, theory, Upstream Oil & Gas, Wang

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.91)

We present a parallel goal-oriented adaptive finite element algorithm that can be used to rapidly compute highly accurate solutions for 2.5D controlled-source electromagnetic (CSEM) and 2D magnetotelluric (MT) modeling problems. The use of an unstructured triangular modeling grid allows for efficient use of mesh nodes for discretizing arbitrarily complex domains. A goal-oriented error estimator based on the dual residual weighting method computed through hierarchical bases provides robust error estimation that is used to guide an iterative adaptive mesh refinement process. Our formulation of the error estimator considers the relative error in the strike aligned fields and their spatial gradients, and therefore results in a more efficient use of mesh nodes than previous error estimators based on absolute field errors. This algorithm has been parallelized over frequencies, transmitters, receivers and wavenumbers, enabling it to achieve accurate solutions in run-times of seconds to tens of seconds for realistic models and data parameters when run on cluster computers of up to a thousand processors. Application of this new algorithm to a complex model that includes strong seafloor topography variations and multiple thin stacked reservoirs demonstrates the performance and scalability on a large cluster computer.

SPE Disciplines:

Technology: Information Technology > Artificial Intelligence > Representation & Reasoning > Mathematical & Statistical Methods (0.43)

Thank you!