Deblending procedure for dense land/OBC/OBN acquisition based on sparsity promoted inverse problem. A general formulation for simulatneous source acquisitions is used to recover data from highly blended acqusition. This procedure uses cutting-edge mathematical tools from Compressed Sensing theory like l1-regularized inverse problem in the curvelet domain to acheive unprecedented deblending results.
Using full-waveform inversion (FWI) to update velocity models that contain salt bodies with high velocity contrasts is challenging. It is even harder if erroneous salt geometry is part of the velocity model. Shen et al. (2017) showed a successful FWI application that corrected some misinterpretation of salt structures and resulted in improved subsalt images at the Atlantis field in the Gulf of Mexico. Their study stressed the importance of the low frequencies (usable down to 1.6 Hz), full azimuths, and wide offsets of OBN data. Encouraged by the success at Atlantis, we revisited some aspects of FWI algorithms to minimize cycle skipping and amplitude discrepancy issues that are common in the presence of salt and salt misinterpretation. Here we present the use of travel time misfit measured in frequency-dependent time windows as the FWI cost function. It is devised to minimize the negative impact from the amplitude discrepancy and cycle-skipping between the recorded data and modeled synthetic data. Furthermore, we use the crosscorrelation coefficient between the recorded data and shifted synthetic data as a weight function in gradient computation to promote travel time measurements of higher quality. We demonstrate the effect of our approach using a staggered full-azimuth streamer data set in an area of complex shallow salt bodies in the Gulf of Mexico.
Imaged based Q-compensation coupled with FWI can solve for significant frequency and amplitude losses below shallow overburden absorptive geologic bodies. Applying Q-compensation to both the baseline and monitor 4D surveys improves the consistency of the 4D response, leading to more reliable placement of development wells. This should lead to enhanced production and improved reservoir management.
In this paper we demonstrate the ability of acoustic land FWI to recover a high-resolution velocity model using reflected waves in addition to diving waves and inverting up to 13 Hz.
In recent years, there have been many rapid developments in subsurface imaging, meaning that even data sets that are only two or three years old can benefit from reprocessing. Reprocessing older data, either on its own or in combination with new data, is both practical and cost-effective as new acquisition can be expensive and time-consuming, especially in areas where there are seasonal constraints due to climate, fishing or breeding seasons. The Cornerstone Evolution project in the Central North Sea demonstrates the value achieved by reprocessing a large number of older surveys in conjunction with newer acquisition.
Machine learning is rapidly becoming a standard technology within the oil and gas industry. This is especially true in petrophysics, where Big Data tend to need more efficient and faster data analysis. The term “machine learning” was coined in 1959 by Arthur Samuel and can be defined as data-driven predictions of behavior rather than rule-based algorithms. Essentially, it is a computer science that uses statistical techniques to give computer systems the ability to learn with data and without being explicitly programmed. A simple example is to record many measurements of the time required for objects of differing attributes to fall various distances and then build a predictive model using linear regression. This predictive model would not be based upon the theory of gravity or the gravitational constant. Instead, through many observations, the model would learn the underlying order in the data. Supplying more data to the model would increase the model’s accuracy. Thus, machine learning models should improve and become better over time as more data become available.
The interpretation of fault surfaces is key to understanding the subsurface geology represented in 3D seismic volumes. The geologic structure represented by seismic reflections can be auto-tracked in the volume. Faults, however, are imaged as discontinuities or changes in curvature in the seismic data. For many years, fault interpretation involved manually picking fault cuts on orthogonal slices through the seismic volume. These fault cuts were grouped into conceptual faults, and 3D fault surfaces were created from the fault cuts.
Many hydrophone-only receiver deghosting approaches assume a stationary free surface profile, or that the data may be represented by linear events within a small spatial aperture. In this paper we propose methods to address these limitations. Firstly, we describe a data-driven methodology to estimate a time-variant free surface profile which may be used in combination with a modified receiver deghosting formulation. Secondly, we propose the use of a tilted hyperboloidal model of the data, which better represents travel-time moveout at short offsets in shallow water regions. Both methods are illustrated on real datasets and demonstrate more effective deghosting than conventional approaches.
The standard processing approach to transform raw shots records into final products suited to geological interpretation involves cascading numerous steps that can be classified in: pre-processing, which aims at correcting the acquisition imperfections or undesired effects (designature, deghosting, geometry), velocity model building which identifies mapping from the data domain to the depth domain and imaging, which applies this mapping. In this paper we show how a Migration Velocity Analysis (MVA) scheme can evolve into a flexible inversion framework that can perform all three steps by inversions. We demonstrate on a 2D real dataset how it handles pre-processing issues and demultiple, as well as providing the velocity model and image domain final products.