Deep learning methods have shown promising performances in predicting acoustic impedance from seismic data which is typically considered as an ill-posed problem for traditional inver- sion schemes. Most of the deep learning methods, however, are based on a 1D neural network which is straightforward to implement but often yields unreasonable lateral discontinuities while predicting a multi-dimensional impedance model trace-by-trace. We introduce an improvement over the 1D network by replacing it with a 2D convolutional neural network (CNN) and incorporating the constraints of an initial impedance model. The initial model is fed to the network to provide a low-frequency trend control, which is helpful for both the 1D and 2D CNNs to yield stable impedance predictions. The proposed 2D CNN architecture is quite simple; however, due to lack of 2D impedance labels, training it is not straightforward. To prepare a 2D training dataset, we first define a random path that passes through multiple well logs. We then follow the path to extract a 2D seismic profile and an initial impedance profile which together form an input to the 2D CNN. The set of well logs (traversed by the path) serves as a partially labeled target. We train the 2D CNN with weak supervision by using an adaptive loss where the output 2D impedance model is adaptively evaluated at the well logs only in the partially labeled target. As the training datasets are randomly extracted in all directions in a 3D survey, the trained 2D CNN can predict a consistent 3D impedance model section-by-section in either inline or crossline directions. Synthetic and field examples show that the proposed 2D CNN is more robust to noise, recovers thin layers better, and yields a laterally more consistent impedance model than a 1D CNN with the same network architecture and the same training logs.
Paleokarst systems are found extensively in carbonate-prone basins worldwide. They can form large reservoirs and provide efficient pathways for hydrocarbon migration, but they can also create serious engineering geohazards. The full delineation of potentially buried paleokarst systems plays an important role for reservoir characterization, oil and gas production, and other engineering tasks. We propose a supervised convolutional neural network (CNN) to automatically and accurately characterize paleokarst and associated collapse features from 3-D seismic images. To avoid time-consuming manual labeling for training the CNN, we propose an efficient workflow to automatically generate numerous 3-D training image pairs including synthetic seismic images and the corresponding label images of the collapsed paleokarst features simulated in the seismic images. With this workflow, we are able to simulate realistic and diverse geologic structures and collapsed paleokarst features in the training images from which the CNN can effectively learn to recognize the collapsed paleokarst features in real field seismic images. Two field examples from the Fort Worth Basin demonstrate that our CNN-based method is superior to conventional automatic methods in delineating paleokarst collapse features from seismic images. From the CNN-based paleokarst characterization, we can further automatically extract 3-D collapsed paleokarst systems and quantitatively measure their geometric parameters. Our CNN-based method is highly efficient and takes only seconds to classify collapsed paleokarst features a 3-D seismic image with 320 × 1, 024 × 1, 024 samples (approximately 268 km 2) by using one graphics processing unit. We propose a convolutional neural network (CNN) to fully detect paleokarst collapse features in 3-D seismic images and extract 3-D paleokarst chimney tubes all at once, which would provide a clear 3-D perspective of
Horizon picking is a fundamental and crucial step for seismic interpretation, but it remains a time-consuming task. Although various automatic methods have been developed to extract horizons in seismic images, most of them may fail to pick horizons across discontinuities such as faults and noise. To obtain more accurate horizons, we propose a dynamic programming algorithm to efficiently refine manually or automatically extracted horizons so that they can more accurately track reflectors across discontinuities, follow consistent phases, and reveal more geologic details. In this method, we first compute an initial horizon using an automatic method, manual picking, or interpolation with several control points. The initial horizon may not be accurate and only needs to follow the general trend of the target horizon. Then, we extract a sub-volume of amplitudes centered at the initial horizon and meanwhile flatten the sub-volume according to the initial horizon. We finally use the dynamic programming to efficiently pick the globally optimal path that passes through global maximum or minimum amplitudes in the sub-volume. As a result, we are able to refine the initial horizon to a more accurate horizon that follows consistent amplitude peaks, troughs, or zero-crossings. As our method does not strictly depend on the initial horizon, we prefer to directly interpolate an initial horizon from a limited number of control points, which is computationally more efficient than automatically or manually picking an initial horizon. In addition, our method is convenient to be interactively implemented to update the horizon while editing or moving the control points. More importantly, these control points are not required to be exactly placed on the target horizon, which makes the human interaction highly convenient and efficient. We demonstrate our method with multiple 2D and 3D field examples that are complicated by noise, faults, and salt bodies.
Subsurface modeling plays an important role in hydrocarbon exploration but remains a challenging task that typically requires a full and reasonable integration of geophysical observations and geologic constraints. We present a workflow to fully utilize seismic amplitudes, well-log properties, and interpreted seismic structures to build geologically reasonable models. We take the Volve field data as an example and apply our workflow step by step as follows: First, we perform some preprocessing on the provided Volve seismic data, horizons, and well-logs to remove anomalous values and adjust seismic-well ties in the depth domain. Second, we use a dynamic-programming based method to infill gaps and refine the vertical positions of the provided horizons and efficiently pick more horizons. We further use the horizon surfaces to interpolate a relative geologic time (RGT) volume which can be considered as an implicit structural model representing seismic structural and stratigraphic features. Third, we integrate the provided well-logs and the computed RGT volume to interpolate a subsurface model that conforms to both well-log properties and seismic struc-tural and stratigraphic features. Finally, we propose a multi-scale neural network to predict a final subsurface model by using a combination of the seismic data and the interpolated model as inputs, and using the well-log data as training labels. Inputing the interpolated model to the network helps to provide a low-frequency control for obtaining a more stable prediction. The results show that our workflow is able to produce geologically reasonable subsurface models with high lateral continuity and vertical resolution.
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