Owen Rohwer Huff

• Rahman, Md Jamilur; Huff, Owen Rohwer; Ahmadigoltapeh, Sajjad; Faleide, Jan Inge; Fawad, Manzar & Mondol, Nazmul Haque (2025). A Simple Method for Estimating Shale Brittleness From Seismic Data: An Example From Offshore Norway. Geophysical Prospecting. ISSN 0016-8025. 73(7). doi: 10.1111/1365-2478.70076. Full text in Research Archive Show summary

  The brittleness of a caprock layer is an important property to measure, as it influences sealing integrity for subsurface fluid injection projects. However, brittleness is a complex function of various factors, such as mineral composition, diagenesis and effective stress, and consequently can vary spatially. Because well log‐based methods for brittleness estimation are often spatially limited, we develop a simple workflow to estimate the brittleness of a shale formation directly from seismic data. The shale in question is the Draupne Formation of the Upper Jurassic age, which acts as a primary seal of Viking Group sandstones in the Horda Platform area of the northern North Sea for hydrocarbon extraction (e.g., the Troll field) and geological CO 2 storage (e.g., Smeaheia). First, well log data from 26 wells in the Horda Platform area is aggregated, focusing on the compressional sonic, bulk density and resistivity values of the Draupne Formation. This data are used to establish a linear model relating the acoustic impedance and elastic properties‐estimated brittleness index (BI) of the Draupne Formation; these two quantities display a correlation of 0.86. By combining this acoustic impedance information with a wavelet extracted from field seismic data and using average acoustic properties for the relatively homogeneous underlying sandstone reservoir, synthetic seismograms corresponding to different BI values of the Draupne Formation are generated. The amplitudes extracted from the synthetic seismograms are then used to establish a quadratic model relating seismic amplitudes at the base Draupne reflection with the BI. Applying this quadratic model on a 2D seismic line from the Stord Basin (south of the Horda Platform) results in BI values that are close to elastic‐properties‐based values at wells which intersect the seismic line and an expected trend of increasing brittleness with respect to depth. This integrated method can be used as part of a workflow to characterize top seal effectiveness, which may be useful in fluid storage prospect evaluation.

• Huff, Owen Rohwer; Evensen, Andreas Kjelsrud; Lie, Jan Erik; Bugge, Aina Juell; Sablon, Ronan & Thorkildsen, Vemund Stenbekk [Show all 7 contributors for this article] (2025). Automatic conditioning of marine seismic angle stack data with a convolutional neural network. Interpretation. ISSN 2324-8858. 13(1), p. T177–T202. doi: 10.1190/INT-2024-0034.1. Full text in Research Archive Show summary

  Misalignment of reflections is a common problem in migrated prestack seismic data, which occurs due to moveout correction with incomplete knowledge of the velocity model. This issue persists in partial angle stack data often used for amplitude-variation-with offset (AVO) analyses and is traditionally treated with residual moveout (RMO) and trim static corrections. However, these methods require time-consuming velocity selection and parameter tuning and commonly lead to spurious alignment of reflections with noise. Therefore, we train a convolutional neural network (CNN) on synthetic examples to automatically perform conditioning and alignment of angle stack data. First, 2D synthetic common-depth point gathers are created using a convolutional modeling method and then made into input-target pairs, where the inputs are poorly moveout corrected (using inaccurate normal-moveout velocities) and the targets are accurately moveout corrected. These input-target examples are split into angle stacks for the near, mid, and far offsets and then used to train a CNN to transform the misaligned angle stacks into their more-aligned counterparts. In testing, the trained network increases the alignment of unseen synthetic data, improving the correlation with the target far-offset trace from 0.76 to 0.85 on average. The network is applied on two field examples from offshore Norway with class 3 and class 2P AVO responses, respectively. In both cases, the angle stack data predicted by the network indicate improved alignment, greater resolution in the far-offset stacked section, and increased correspondence to a well-tie synthetic seismogram, all while retaining the AVO responses. The CNN predictions are compared with angle stack data after traditional conditioning (RMO and trim static corrections), with the results being of similar or greater quality. This method enables high-quality conditioning of angle stack data at a fraction of the time required for conventional methods and is easily adaptable to different data sets.

• Huff, Owen Rohwer; Lie, Jan Erik; Evensen, Andreas Kjelsrud & Bugge, Aina Juell (2024). Leveraging source-over-cable marine seismic field data for near offset reconstruction with deep learning. SEG technical program expanded abstracts. p. 2028–2032. doi: 10.1190/image2024-4100715.1. Full text in Research Archive
• Huff, Owen Rohwer; Thorkildsen, Vemund Stenbekk; Greiner, Thomas Andre Larsen; Lie, Jan Erik; Evensen, Andreas & Bugge, Aina Juell [Show all 7 contributors for this article] (2024). Near offset reconstruction for marine seismic data using a convolutional neural network. Geophysical Prospecting. ISSN 0016-8025. 72(6), p. 2164–2185. doi: 10.1111/1365-2478.13505. Full text in Research Archive Show summary

  Marine seismic data is often missing near offset information due to separation between the source and receiver cables. To solve this problem, a convolutional neural network is trained on synthetic seismic data to reconstruct the near offset gap. The synthetic data is created using a two-dimensional finite difference method within a heterogeneous velocity model. These synthetics are generated with a source-over-receiver acquisition geometry so that they contain complete near offset data. The convolutional neural network is then trained on input-target synthetic pairs where the inputs are common midpoint gathers with the near offset section removed, and the targets are the same gathers with the near offset section retained. Following training, the robustness of the method is investigated with regards to common midpoint data sorting, normal moveout correction and changes in the velocity model. It is found that training on common midpoint-sorted data results in 2.8 times lower error than training on shot gathers, that normal moveout correction of the training data makes no significant difference in error levels, and that the model can reconstruct realistic near offsets on synthetic data generated 10 km away within the heterogeneous velocity model. In field data testing, first a dataset with source-over-cable acquisition geometry from the Barents Sea is used to compare the reconstructed wavefields to ground truth values. Although the reconstructed amplitudes require minor scaling to match the true values, predictions on this dataset yield 2.5 times lower near offset reconstruction error compared to a simple Radon transform interpolation method. Furthermore, amplitude versus offset gradient and intercept sections from the Barents Sea dataset are estimated with half the error when including the convolutional neural network-predicted near offset data, compared to only using the conventionally-acquirable portion of the data (beyond 112.5 m of offset). In a secondary field data test, a conventional northern North Sea dataset is used to demonstrate how the method may be applied in practice. Here, the convolutional neural network generates more realistic predictions than the Radon method, and the gradient and intercept sections calculated using the convolutional neural network-predicted traces have higher signal-to-noise ratios than the sections calculated using only the original data. The combination of high-quality synthetic training data and interpolation in the common midpoint domain enables near offset reconstruction at significant depth (1 s of two-way traveltime or more), which is demonstrated in both synthetic and field examples.

• Liu, Youfang; Huff, Owen Rohwer; Luo, Bin; Jin, Ge & Simmons, James (2022). Convolutional neural network-based classification of microseismic events originating in a stimulated reservoir from distributed acoustic sensing data. Geophysical Prospecting. ISSN 0016-8025. 70(5), p. 904–920. doi: 10.1111/1365-2478.13199. Full text in Research Archive

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• Huff, Owen Rohwer; Evensen, Andreas Kjelsrud; Lie, Jan Erik; Bugge, Aina Juell; Sablon, Ronan & Thorkildsen, Vemund Stenbekk (2024). Efficient Angle Stack Conditioning Using a Convolutional Neural Network. 85th EAGE Annual Conference & Exhibition. 2024, p. 1–5. doi: 10.3997/2214-4609.202410580. Full text in Research Archive
• Huff, Owen Rohwer; Thorkildsen, Vemund Stenbekk; Greiner, Thomas Andre Larsen; Lie, Jan Erik; Bugge, Aina Juell & Evensen, Andreas Kjelsrud (2023). Convolutional Neural Network-based Restoration of Near Offsets in Marine Seismic Data. 84th EAGE Annual Conference & Exhibition. 1, p. 1–5. doi: 10.3997/2214-4609.202310843. Full text in Research Archive

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