Inter-source seismic interferometry by multidimensional deconvolution (MDD) for borehole sources

Seismic interferometry (SI) is usually implemented by crosscorrelation (CC) to retrieve the impulse response between pairs of receiver positions. An alternative approach by multidimensional deconvolution (MDD) has been developed and shown in various studies the potential to suppress artifacts due to irregular source distribution and intrinsic loss. Following previous theories on SI by MDD, we extend it to retrieve the impulse response between pairs of source positions by invoking source and receiver reciprocity. We verify the theory using a simple two-layered model and show that the retrieved response by MDD is more accurate than that by CC, and furthermore, it is free of free-surface multiples. We discuss the necessary pre-processing required for this method. This inter-source SI approach creates a virtual acquisition geometry with both borehole sources and receivers without the need to deploy receivers in the borehole, which might be of interest to applications such as seismic while drilling (SWD)

Elastodynamic full waveform inversion on GPUs with time-space tiling and wavefield reconstruction

Full waveform inversion (FWI) is a procedure used to determine the elastic parameters of the Earth by reducing the misfit between observed elastodynamic wavefields and their numerically modelled counterparts. The numerical solution of the elastodynamic wave equation is computationally expensive, and its performance is typically bandwidth bound. Computing the gradient of the FWI misfit functional adds further complexity as it involves computing the zero-lag cross-correlation of two wavefields propagating in opposite temporal directions. In this paper, we utilize graphics processing units (GPUs) for their high memory bandwidth and combine two principal optimizations in order to compute FWI gradients on large models and for long simulation times. Wavefield reconstruction methods allow efficient gradient computations with minimal memory requirements and interconnection transfers. Time-space tiling techniques permit us to transcend the limited amount of GPU memory while avoiding dramatic slowdowns due to the low interconnection bandwidth. The implementation considers a task-oriented, hybrid usage of explicitly managed and Unified Memory in order to satisfy the requirements. Benchmarks demonstrate that the proposed approach is able to preserve 78–90% of the original performance, when oversubscribing the amount of physical memory available on GPUs. Comparison with existing methods highlights the benefits of the method

Retrieving virtual reflection responses at drill-bit positions using seismic interferometry with drill-bit noise

In the field of seismic interferometry, researchers have retrieved surface waves and body waves by cross-correlating recordings of uncorrelated noise sources to extract useful subsurface information. The retrieved wavefields in most applications are between receivers. When the positions of the noise sources are known, inter-source interferometry can be applied to retrieve the wavefields between sources, thus turning sources into virtual receivers. Previous applications of this form of interferometry assume impulsive point sources or transient sources with similar signatures. We investigate the requirements of applying inter-source seismic interferometry using non-transient noise sources with known positions to retrieve reflection responses at those positions and show the results using synthetic drilling noise as source. We show that, if pilot signals (estimates of the drill-bit signals) are not available, it is required that the drill-bit signals are the same and that the phases of the virtual reflections at drill-bit positions can be retrieved by deconvolution interferometry or by cross-coherence interferometry. Further, for this case, classic interferometry by cross-correlation can be used if the source power spectrum can be estimated. If pilot signals are available, virtual reflection responses can be obtained by first using standard seismic-while-drilling processing techniques such as pilot cross-correlation and pilot deconvolution to remove the drill-bit signatures in the data and then applying cross-correlation interferometry. Therefore, provided that pilot signals are reliable, drill-bit data can be redatumed from surface to borehole depths using this inter-source interferometry approach without any velocity information of the medium, and we show that a well-positioned image below the borehole can be obtained using interferometrically redatumed reflection responses with just a simple velocity model. We discuss some of the practical hurdles that restrict the application of the proposed method offshore

This content has been downloaded from IOPscience. Please scroll down to see the full text. Data-driven inversion/depth imaging derived from approximations to one-dimensional inverse acoustic scattering Data-driven inversion/depth imaging derived from appr

Abstract This paper presents a new mathematical framework based on inverse scattering for the estimation of the scattering potential and its nature of a one-dimensional acoustic layered medium from single scattering data. Given the Born potential associated with constant-velocity imaging of the single scattering data, a closedform implicit expression for the scattering potential is derived in the WKBJ and eikonal approximations. Adding physical insight, the WKBJ and eikonal solutions can be adjusted so that they conform to the geometrically derived precise solutions of the one-dimensional scattering problem recently suggested by the authors. In a layered medium, the WKBJ and eikonal approximations, in addition to providing an implicit solution for the scattering potential, provide an explicit estimate of the potential, not within the actual potential discontinuities (layer interfaces), but within the Born potential discontinuities derived by the constant-velocity imaging. This estimate of the potential is called the 'squeezed' potential since it mimics the actual potential when the depth axis is squeezed so that the discontinuities of the actual potential match those of the Born potential. It is shown that the squeezed potential can be estimated by amplitude-scaling the Born potential by an amplitude function of the Born potential. The accessibility of the squeezed potential makes the inverse acoustic scattering problem explicit and non-iterative since the estimated squeezed potential can non-linearly be stretched with respect to the depth axis so that the potential discontinuities are moved towards their correct depth location. The non-linear stretch function is a function of the Born potential. The solution 5 Professor Emeritus

CO2 saturation estimates at Sleipner (North Sea) from seismic tomography and rock physics inversion

acceptedVersio

Up- and downgoing borehole wavefield retrieval using single component borehole and reflection data

A standard procedure in processing vertical seismic profile (VSP) data is the separation of up- and downgoing wavefields. We show that these wavefields in boreholes can be retrieved using only single-component data, given that a full set of surface reflection data is also available. No medium parameters are required. The method is an application of the Marchenko method and uses a focusing wavefield. It is a wavefield that satisfies certain focusing conditions in a reference medium. We show that the method is applicable to boreholes with any orientation, and no receiver array is required. By this work, we present two contributions. One is that we investigate the effect of using only the traveltime from borehole data to form the focusing wavefield. The second is that we validates standard separation methods (PZ summation and f-k filtering) by retrieving the one-way wavefields from a completely different approach. We use the numerically modelled data from a realistic field velocity model in the North Sea. Three borehole geometries (horizontal, deviated and vertical) are tested. We discuss the practical aspects for field application in the end

Wavefield reconstruction for velocity–stress elastodynamic full-waveform inversion

Gradient computations in full-waveform inversion (FWI) require calculating zero-lag cross-correlations of two wavefields propagating in opposite temporal directions. Lossless media permit accurate and efficient reconstruction of the incident field from recordings along a closed boundary, such that both wavefields propagate backwards in time. Reconstruction avoids storing wavefield states of the incident field to secondary storage, which is not feasible for many realistic inversion problems. We give particular attention to velocity–stress modelling schemes and propose a novel modification of a conventional reconstruction method derived from the elastodynamic Kirchhoff–Helmholtz integral. In contrast to the original formulation (in a previous related work), the proposed approach is well-suited for velocity–stress schemes. Numerical examples demonstrate accurate wavefield reconstruction in heterogeneous, elastic media. A practical example using 3-D elastic FWI demonstrates agreement with the reference solution

Artificial Intelligence for Well Integrity Monitoring Based on EM Data

Monitoring of integrity of plugged and abandoned (P&A'ed) wells is of interest for the oil and gas industry and for CO2 storage. The purpose of this study is to develop artificial intelligence (AI)-based approaches to detect anomalies or defects when monitoring permanently plugged wells. The studied solution is based on the analysis of electromagnetic (EM) data. We consider an offshore setting where the EM signal is generated in presence of a P&A'ed well and the resulting electric field is recorded at the seafloor. Numerical simulations are used to train an AI algorithm to classify the modelled EM features into predefined well integrity classes. We consider four scenarios: (1) no well, (2) well with three 20 meters thick cement barriers of thickness, (3) well with three cement barriers of 60 meters thickness, and (4) well with three cement barriers of 100 meters thickness. Convolutional neural networks (CNNs) are tested as the AI algorithm in this study. After training the algorithm on 80% of the data, it shows an accuracy of 95.36% on the test data. P&A'ed well integrity monitoring currently remains limited to local observation and symptom identification, but this study shows that there is great potential for developing remote non-invasive well integrity monitoring techniques