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The quality of time-lapse analysis depends highly on the repeatability of the acquisition. However, in practice, it is almost impossible to perfectly mimic a base survey due to environmental conditions and inaccurate measurements. Lack of repeatability often results in 4D noise, which may compromise the 4D signal. In the presence of subsidence, caused by the depletion of the reservoir, 4D signal exists outside of the reservoir area, and its extraction from noisy 4D data can be challenging without a priori information. Water layer tomography has already been proposed to recover uncertain parameters from the acquisition in order to address the non-repeatability effects in the data but not with as many parameters as presented in this paper: water velocity, source position, top of the water layer and start of data time. Unlike most water layer tomography, our method not only relies on the inversion of the water-bottom primary but also of the first-order multiple travel times picked in the data. An application to a 3D deep-water survey offshore Angola is presented. The flow is applied independently to all vintages of a 4D project resulting in significant reduction of the 4D noise and a clear visibility of the subsidence.

Sub-basalt seismic imaging is very challenging due to large impedance contrasts at sediment-basalt interfaces. The impedance of basalt usually gives a strong reflection coefficient at the top of basalt, and thus generates strong multiples. Offshore western India, this issue is compounded by short-period seabed multiples generated by the shallow sea floor. Moreover, the presence of the basalt layer limits the angle of reflections from sub-basalt structures, making velocity modeling difficult. The combination of strong, complex multiples and the challenges of obtaining a reliable velocity model gives rise to poor imaging beneath and within the basalt. In this study, a comprehensive pre-migration demultiple flow was devised to tackle the strong surface and interbed multiples. For velocity model building, full-waveform inversion (FWI) was applied for the shallow velocity update and non-linear scanning tomography was then utilized to update the velocity within and beneath the basalt layer. Due to the poor initial velocity model, an enhanced dynamic-warping FWI approach was used to mitigate the cycle-skipping issue, and the maximum FWI frequency was extended to 20 Hz. With the benefits from the comprehensive demultiple process and advanced velocity model building, imaging of the complex sub-basalt structures in this area was improved.

The attenuation of surface related multiples is typically one of the most challenging steps in the processing of shallow water marine projects. Least-squares wave-equation deconvolution imaging is a powerful tool to address this challenge, but images derived from a deep target level may produce sub-optimal demultiple results for the shallower section. We introduce image domain sparseness weights to the least-squares problem, derived from a water-bottom depth estimate. This provides a reflectivity with a sharp contrast at the water-bottom, and the corresponding multiple prediction exhibits improved temporal resolution compared to least-squares wave-equation deconvolution imaging. We also illustrate how the multiple prediction from sparse wave-equation deconvolution imaging may be combined with source-side targeted multiple prediction to improve multiple attenuation for complex multiple generators. Data examples from the Central North Sea and the West of Shetland confirm the benefits of the proposed methods in attenuating residual multiples.

The Taranaki Basin is one of New Zealand’s largest basins. Initially forming as a Cretaceous rift basin, it has over 400 exploration and production wells. Early basin history is characterized by extensional fault blocks, and as basin evolution continued, thrusting and inversion associated with the convergent active margin set up trapping mechanisms for petroleum accumulations. Recent fault blocks within shallow Plio-Pleistocene sediments exhibit strong azimuthal anisotropy. Without considering this effect, seismic imaging in the area suffers structural discontinuity and fault misplacement. Below these fault blocks, it is challenging to image the thrust system and sub-thrust structures due to poor illumination from strong velocity variation around the thrust. To overcome these challenges, we focused on two major aspects. First, we built a tilted orthorhombic (TORT) velocity model to handle the strong azimuthal anisotropy in the overburden. At the time, we only had access to narrow-azimuth (NAZ) data, thus we derived the TORT parameters through scanning based on stack and gather responses. Second, we applied least-squares (LS) TORT Q-RTM to honor azimuthal anisotropy and compensate for poor illumination from the complex velocity. We observed significant imaging uplifts compared with the vintage data that subsequently provided an improved geological interpretation.

The desire for ultra-high density (UHD) seismic surveys is now becoming more achievable for future exploration and field development with the increasing availability of versatile nodal land systems. Acquisition geometry design using a higher density of sources as well as receivers considerably reduces the effects of spatial aliasing and also provides better subsurface illumination. By sampling the wavefield more densely, there are improved recordings of both signal and noise. This presents new opportunities for processing and imaging. We use a recent UHD nodal survey with nominal trace density approaching 200 times that of typical conventional cable-based surveys to discuss the challenges and rewards.

A high-resolution seismic image is of great importance to exploration and production in many ways, such as bypassing drilling hazards and identifying compartmentalized reservoirs. To achieve seismic resolution as high as possible, the conventional seismic imaging process takes more of a linear approach to deal with one or a few specific issues at a time, such as noise and multiple attenuation, source and receiver deghosting, velocity errors, illumination holes, and migration swings. Full-waveform inversion (FWI) Imaging models and uses the full-wavefield data, including primaries and multiples (ghost included) and reflection and transmission waves, to iteratively invert for the reflectivity together with velocity and thus is an elegant solution to resolve those issues in one (iterative) inversion. FWI Imaging has proven to be a superior method for providing seismic images of greatly improved illumination, S/N, focusing, and thus better resolution, over conventional imaging methods. We demonstrate with a towed-streamer data set and an OBN data set that FWI Imaging with a frequency close to the temporal resolution limit of seismic data (100 Hz or higher) can provide seismic images of unprecedented resolution from the recorded seismic data, which has been impossible to achieve with conventional imaging methods.

Images from Kirchhoff migration can suffer from uneven illumination and contamination by migration artefacts. One of the issues is that migration is not a true inverse operation – it is based on the adjoint of the forward modelling operator. In contrast, least squares migration approximates the inverse of the forward modelling and hence the impact of detrimental effects on the image can be reduced. Here, we describe benefits of a non-iterative Kirchhoff least squares method (migration deconvolution). We present a workflow and demonstrate that the method can be used to attenuate image artefacts, help balance image illumination, and increase clarity of AVO attributes.

4D time-lapse seismic imaging is typically performed using the same velocity model to migrate the baseline and monitor data. However, in complex cases where one producing reservoir sits above another, significant changes in the properties of the overlying reservoir can result in a 4D signal with associated 4D coda immediately below it, which would mask the 4D signal of reservoirs underneath. Resolving such an issue requires that the baseline and monitor data be migrated with separate models. We demonstrate how we have used a visco-acoustic full-waveform inversion to resolve 4D changes in velocity and absorption within an overburden gas-charged channel. The resulting 4D image shows minimisation of 4D coda below the overburden channel and the unveiling of 4D signals at deeper targets of interest.

Convolutional based deep neural networks can be used in addition to existing workflows, to improve turnaround or as a ‘guide’ for further processing. Whilst a lot of effort has been made to try to improve the DNN architecture for processing tasks or to understand their physical interpretation, the choice of the training-set is rarely discussed. For a good quality DNN result, the training-set must be representative of the variability (or statistical diversity) of the full dataset, and the question of the choice of this dataset for seismic data is discussed in this paper. We present two methods for the selection of the training set. The first one is based on proxy attributes and their clustering. Our clustering approach is not only using the clusters themselves but also the information on the distance to the centroid for the cluster definition. The other method is based on the data themselves. It starts from a predefined training set and then scans through the full dataset to identify additional training points that will be used to augment the initial training set.