Ocean Bottom Node Acquisition Research Articles

Free-surface multiple attenuation and seismic deghosting for blended data using convolutional neural networks

Simultaneous source acquisition has become common over the past few decades for marine seismic surveys because of the increased efficiency of seismic acquisition by limiting the time, reducing the cost, and having less environmental impact than conventional single-source (or unblended) acquisition surveys. For simultaneous source acquisition, seismic sources at different locations are fired with time delays, and the recorded data are referred to as the blended data. The air-water interface (or free surface) creates strong multiples and ghost reflections for blended seismic data. The multiples and/or ghost reflections caused by a source in the blended data overlap with the primary reflections of another source, thus creating a strong interference between the primary and multiple events of different sources. We develop a convolutional neural network (CNN) method to attenuate free-surface multiples and remove ghost reflections simultaneously from the blended seismic data. The CNN-based solution that we develop operates on single traces and is not sensitive to the missing near-offset traces, missing traces, and irregular/sparse acquisition parameters (e.g., for ocean-bottom node acquisition and time-lapse monitoring studies). We illustrate the efficacy of our free-surface multiple attenuation and seismic deghosting method by presenting synthetic and field data applications. The numerical experiments demonstrate that our CNN-based approach for simultaneously attenuating free-surface multiples and removing ghost reflections can be applied to the blended data without the deblending step. Although the interference of primaries and multiples from different shots in the blended data makes free-surface multiple attenuation harder than the unblended data, we determine that our CNN-based method effectively attenuates free-surface multiples in the blended synthetic and field data even when the delay time for the blending is different in the training data than in the data to which the CNN is applied.

Research note: Application of refraction full‐waveform inversion of ocean bottom node data using a squared‐slowness model parameterization

AbstractFull‐waveform inversion is a wave equation–based imaging technique for obtaining subsurface model parameters by matching modelled with field data. Full‐waveform inversion is often formulated as a local optimization problem in which the model parameterization influences the gradient preconditioner and the convergence rate associated with the full‐waveform inversion objective function. Model parameterization governs the radiation pattern of the so‐called secondary Born source. In this work, we assess model parameterization effects on the estimation of P‐wave velocities using a three‐dimensional acoustic time‐domain full‐waveform inversion procedure. These include the three commonly used parameterization: velocity, slowness and squared slowness. In this context, we consider a field data set from a deepwater Brazilian pre‐salt field using a recently introduced circular shot ocean bottom node acquisition which favours refracted waves. The results reveal that the squared slowness model parameterization provides a satisfactory trade‐off between the reconstruction of the deep pre‐salt target area and convergence rate, saving 50% of runtime compared to the velocity and slowness cases.

Optimal Seismic Sensor Placement Based on Reinforcement Learning Approach: An Example of OBN Acquisition Design

Seismic acquisition costs are directly associated with the number of sensors used in the survey. Limiting the number of sensors in a seismic survey can be beneficial, especially when sensors are expensive to purchase, deploy, and maintain. This work explores an optimal design method for ocean bottom node (OBN) detector deployment. The proposed method is based on a reinforcement learning approach. We assume access to an initial dataset over the area of study. These data are used to extract an over-complete pre-learned basis library via the proper orthogonal decomposition method, which leads to a fast least-squares seismic data reconstruction algorithm. Then, the sensor selection procedure entails using Q-learning to find the sensor configuration that maximizes the reconstruction quality.

Illumination guided sparse geometry optimization for target-oriented full-waveform inversion: An ocean bottom node synthetic study

For full-waveform inversion (FWI) with sparse survey layout, it is crucial to optimize the source and receiver geometry toward full-wave illumination. We propose a sparse geometry optimization method and workflow for target-oriented FWI using finite-frequency illumination analysis. The frequency-dependent impact of a source-receiver pair to the wavefield sensitivity kernel and diagonal terms of approximate Hessian matrix are derived under visco-acoustic model assumptions. Based on the reciprocity principle, we use virtual sources in target zone to efficiently calculate the Green's function and illumination from acquisition surface. Optimal locations of sparse receivers are determined by comparing band-limited illumination at reference stations and their surrounding region through an illumination attribute scan. FWI model residual errors are used to evaluate the performance of optimized geometry layout for a given target zone. The performances of different geometries are compared to determine a suitable frequency band for FWI geometry optimization. Synthetic deep water model and ocean bottom node acquisition geometry are used to verify the feasibility of the workflow. Numerical tests in target-oriented FWI show that optimized geometry with constraints of band-limited illumination below10 Hz has better performance than that above 10 Hz. An extension of our geometry optimization method to sparse source configuration is straightforward.

A review of OBN processing: challenges and solutions

Abstract In the last decade, a significant shift in the marine seismic acquisition business has been made where ocean bottom nodes gained a substantial market share from streamer cable configurations. Ocean bottom node acquisition (OBN) can acquire wide azimuth seismic data over geographical areas with challenging deep and shallow bathymetries and complex subsurface regimes. When the water bottom is rugose and has significant elevation differences, OBN data processing faces a number of challenges, such as denoising of the vertical geophone, accurate wavefield separation, redatuming the sparse receiver nodes from ocean bottom to sea level and multiple attenuation. In this work, we review a number of challenges using real OBN data illustrations. We demonstrate corresponding solutions using processing workflows comprising denoising the vertical geophones by using all four recorded nodal components, cross-ghosting the data or using direct wave to design calibration filters for up- and down-going wavefield separation, performing one-dimensional reversible redatuming for stacking QC and multiple prediction, and designing cascaded model and data-driven multiple elimination applications. The optimum combination of the mentioned technologies produced cleaner and high-resolution migration images mitigating the risk of false interpretations.

The impact of acquisition geometry on full-waveform inversion updates

Full-waveform inversion (FWI) is a high-resolution model-building technique that uses the entire recorded seismic data content to build the earth model. Conventional FWI usually utilizes diving and refracted waves to update the low-wavenumber components of the velocity model. However, updates are often depth limited due to the limited offset range of the acquisition design. To extend conventional FWI beyond the limits imposed by using only transmitted energy, we must utilize the full acquired wavefield. Analyzing FWI kernels for a given geology and acquisition geometry can provide information on how to optimize the acquisition so that FWI is able to update the velocity model for targets as deep as basement level. Recent long-offset ocean-bottom node acquisition helped FWI succeed, but we would also like to be able to utilize the shorter-offset data from wide-azimuth data acquisitions to improve imaging of these data sets by developing the velocity field with FWI. FWI models are heading toward higher and higher wavenumbers, which allows us to extract pseudoreflectivity directly from the developed velocity model built with the acoustic full wavefield. This is an extremely early start to obtaining a depth image that one would usually produce in much later processing stages.

Computational aspects of finite-frequency traveltime inversion kernels

Finite-frequency traveltime inversion offers higher accuracy for velocity model building than ray-based traveltime inversion. The adjoint force is the key for the computation of inversion kernels. Starting at the definition of inversion kernels for the acoustic wave equation, we have derived the explicit formula for the spectral distribution density function used in the adjoint force computation. Two formulations are developed for the computation of adjoint forces for receiver-side extrapolation, frequency-domain representation, and time-domain representation. The accuracy of finite-frequency traveltime inversion kernels is benchmarked with the analytical solutions for homogeneous isotropic media. We use wavefront construction to compute the first Fresnel zones for kernel conditioning. Based on dynamic ray tracing, we design a processing procedure guided by synthetic data tests to extract the desired events for wavefield backward extrapolation from the data. Unlike ray-based velocity tomography, finite-frequency inversion can resolve the velocity structures comparable with the size of Fresnel zones as we demonstrate on a marine salt model using ocean-bottom node acquisition geometry. Despite the fact that the inversion kernels are based on Born approximation, velocities with errors up to 20% can be well-resolved. For practical purposes, a simple formulation is given for the determination of the shot spacing. Our workflow for finite-frequency inversion is efficient and converges in only very few iterations.

First time-lapse OBN in Bonga deepwater offshore field

Time-lapse seismic data from Bonga Field, located in deepwater Nigeria, have delivered excellent results previously from dedicated streamer seismic surveys (2000, 2008, and 2012) that image changes in amplitude due to water replacing oil. One challenge with the streamer data is the presence of a floating production storage and offloading (FPSO) unit. It is difficult to accurately repeat streamer acquisition geometry in economically important updip regions of reservoirs that lie beneath the FPSO. To ensure an accurate repeat of this area, we acquired an ocean-bottom node (OBN) survey in 2010 and carried out the first time-lapse OBN repeat of the survey in 2018. Time-lapse processing of the OBN data produced excellent results. We obtained an OBN-on-OBN normalized root mean square (NRMS) difference repeatability of 6%. This was an improvement over the 12% NRMS for streamer-on-streamer repeats. The OBN data quality was unaffected by the FPSO, enabling us to properly image the changes occurring in this area. The 4D OBN interpretation was used to identify bypassed oil opportunities. The data also were used to derisk well placement, optimize water injection, and update the dynamic reservoir models. The new models are essential to enhance predictability and field performance. We also analyzed an area northwest of the field where we extended the 2018 OBN acquisition and compared it to streamer data to optimize a new injection well location.

On the role of diffractions in velocity model building: a full-waveform inversion example

Imaging of small-scale heterogeneities is important for the geological exploration in complex environments. It requires a processing sequence tuned to high-resolution model building. Conventional methods which use refractions or reflections might face problems in resolving small-scale features since they are visually close to the resolution of the reflection images. Additional information or an unconventional technology, which supports the reflection imaging, is thus of great interest. An unconventional method based on seismic diffractions naturally complements specular reflection imaging. Diffracted waves represent a direct seismic response from small-scale subsurface heterogeneities, such as inclusions with a characteristic size of the prevailing wavelength, or discontinuities in geological interfaces, such as faults and fractures. We investigate the rule of diffracted part of the wavefield on velocity model building using a full-waveform inversion (FWI) example. In order to best acknowledge refracted and reflected parts of the wavefield in FWI, we chose a synthetic data example which mimics the ocean-bottom nodes acquisition survey as it provides almost perfect conditions for FWI of diving waves, a standard tool for high-resolution model building. We show, that FWI using diving waves produces a well-resolved anomaly. Including other part of the wavefield, reflected waves, further improves the resolution of the velocity anomaly but also leads to a gentle overfitting due to missing illumination from the very steep anomaly flanks. Considering diffracted events in FWI improves the model resolution even further resulting in a detailed velocity model and correctly imaged anomaly in both vertical and lateral directions.