Gboyega Ayeni

Target-oriented joint least-squares migration/inversion of time-lapse seismic data sets

Two related formulations are proposed for target-oriented joint least-squares migration/inversion of time-lapse seismic data sets. Time-lapse seismic images can be degraded when reservoir overburden is complex or when acquisition geometries significantly differ, because the migration operator does not compensate for the resulting amplitude and phase distortions. Under these circumstances, time-lapse amplitudes are poor indicators of production-related changes in reservoir properties. To correct for such image degradation, time-lapse imaging is posed as joint inverse problems that utilize concatenations of target-oriented approximations to the linear least-squares imaging Hessian. In both formulations, spatial and temporal constraints ensure inversion stability and geologically consistent time-lapse images. Using two numerical time-lapse data sets, we confirmed that these formulations can attenuate illumination artifacts caused by complex overburden or geometry differences, and that they yield better-quality images than obtainable with migration.

Joint preconditioned least‐squares inversion of simultaneous source time‐lapse seismic data sets

We present a joint least-squares inversion method for imaging simultaneous source (or blended) time-lapse seismic data sets. Non-repeatable shot and receiver positions introduce undesirable artifacts into time-lapse seismic images. We conjecture that more artifacts will result from relative shot-timing nonrepeatability when data sets are acquired with several simultaneously shooting sources. We show that these artifacts can be attenuated by joint inversion of such data sets without need for initial separation. Preconditioning with non-stationary dip filters and with temporal smoothness constraints ensures stability and geologically consistent time-lapse images. Results from a modified Marmousi 2D model show that this method yields reliable time-lapse images.

On the separation of simultaneous‐source data by inversion

Simultaneous-source data can be adequately separated using an inversion formulation. To recover component shot records, we formulate the data-separation as a simultaneous Radon inversion problem. By minimizing the resulting objective function with a robust hybrid solver, we obtain high-quality estimates of the component shot records. Furthermore, regularization with directional Laplacians improves the data quality. In our approach, we estimate a single model that predicts all recorded data, and we treat all components of the recorded data as signal. Our method can be applied to any number of sources within a single survey and can be easily extended to multiple (time-lapse) surveys. Using 2D sections the SEAM model and simultaneous-source data from a 2D land data set, we show that our method can give results of quality comparable to the reference independent shot records.

A Preconditioning Scheme For Full Waveform Inversion

The waveform inversion problem is inherently ill-posed. Traditionally, regularization terms are used to address this issue. For waveform inversion where the model is expected to have many details reflecting the physical properties of the Earth, regularization and data fitting can work in opposite directions: the former smoothing and the later adding details to the model. In this paper, we constrain the velocity model with a model-space preconditioning scheme based on directional Laplacian filters. This preconditioning strategy preserves the details of the velocity model while smoothing the solution along known geological dips. The Laplacian filters have the property to smooth or kill local planar events according to a local dip field. By construction, these filters can be inverted and used in a preconditioned waveform-inversion scheme to yield geologically meaningful models. We illustrate on a 2-D synthetic example how preconditioning with non-stationary directional Laplacian filters outperforms traditional waveform inversion when sparse data are inverted for. We think that preconditioning could benefit waveform inversion of real data where (for instance) irregular geometry, coherent noise and lack of low frequencies are present.

Optimized Local Matching of Time-lapse Seismic Data: A Case Study From the Gulf of Mexico

We discuss a method for selecting optimal filter lengths, trace segments and damping parameters for local-matching of timelapse seismic data sets. In this method, an evolutionary programming (EP) algorithm is used to optimize parameters such that estimated match filters have predefined properties within and outside the estimation window. Results from a 3D timelapse data set from the Gulf of Mexico show that this method provides improvements over conventional local-matching that use fixed, manually selected filter estimation parameters.

Wave‐equation inversion of time‐lapse seismic data sets

We propose a linearized wave-equation inversion formulation for time-lapse data sets. Our method poses time-lapse imaging as a joint least-squares problem that utilizes target-oriented approximations to the Hessian of the objective function. Because our method accounts for differences in acquisition geometries and illumination patterns, and band-limited wavepropagation effects, it provides better estimates of productionrelated changes than migration. Using data sets from a North Sea field, we demonstrate how our method can be used to image differences between time-lapse data sets. We show that obstruction artifacts may be attenuated by wave-equation inversion.

Cyclic 1D Matching of Time-lapse Seismic Data Sets: A Case Study of the Norne Field

Seismic cross-equalization attenuates unwanted or nonproduction-related artifacts in time-lapse seismic data sets. Two robust post-imaging cross-equalization methods are considered. First, an efficient cyclic 1D warping method estimates apparent displacement vectors between baseline and monitor images. To obtain accurate production-related time-lapse images, all displacement components must be considered. Next, a cyclic 1D match-filtering method attenuates residual artifacts using optimal filtering parameters. Application to the Norne time-lapse seismic data set shows that these methods together form a powerful time-lapse cross-equalization scheme.

Seismic reservoir monitoring with permanent encoded seismic arrays

Hydrocarbon reservoirs can be efficiently monitored with encoded data from permanent seismic arrays. Permanent seismic arrays can yield a vast amount of data that may enable near–real-time monitoring. Because these vast data volumes pose operational, storage and processing challenges, an encoding approach is desirable. Although data encoding introduces cross-talk artifacts, permanent arrays allow for high-repeatability of such artifacts, making time-lapse crossequalization easy. Furthermore, stacking data from several encoded intermittent seismic sources improve the signal-tonoise ratio. Using a 2D numerical model, we show that this method can give reliable time-lapse images that are comparable to those from conventional single-source data sets.

Joint target-oriented wave-equation inversion of multiple time-lapse seismic data sets

We propose a joint target-oriented wave-equation inversion method for multiple time-lapse seismic data sets. Complex reservoir overburden or acquisition geometry difference degrade the quality of time-lapse seismic images. This degradation occurs because the imaging operator does not account for overburden and geometry artifacts. Under such conditions, time-lapse images are poor indicators of production-related changes in reservoir properties. To solve this problem, we pose time-lapse imaging as a joint linear inverse problem that utilize concatenations of target-oriented approximations to the least-squares imaging Hessian. The proposed method outputs a baseline image and time-lapse images from multiple seismic data sets. Using a 2D synthetic sub-salt model, we show that this method attenuates overburden and geometry artifacts and that it gives reliable time-lapse seismic images.