During drilling and logging, depth alignment of well logs acquired in the same borehole section at different times is a vital preprocessing step before any petrophysical analysis. Depth alignment requires high precision as depth misalignment between different log curve measurements can substantially suppress possible correlations between formation properties, leading to imprecise interpretation or even misinterpretation. Standard depth alignment involves cross correlation, which typically requires user intervention for reliability. To improve the depth alignment process, we apply deep-learning techniques and propose a simple and practical implementation of a one-dimensional (1D) supervised convolutional neural network (1D CNN). We train seven CNN models using different log measurements, such as gamma ray, resistivity, P- and S-wave sonic, density, neutron, and photoelectric factor (PEF), to estimate depth mismatches between the corresponding raw logging-while-drilling (LWD) and electrical-wireline-logging (EWL) logs of each measurement type. Our deep-learning approach avoids manual feature extraction; hence, no high-level petrophysical knowledge is needed by our algorithms. We use log data from six wells from the Ivar Aasen Field in the Norwegian North Sea. Four of the six wells constitute the entire data set for training and model selection, in which we compare three search algorithms during the hyperparameter tuning. Only two wells have both LWD and EWL log suites. These wells are used for depth-shift inference. We focus on estimating bulk shifts, and we assume the existence of small pattern differences. We assess our results by visual inspection and quantitative metrics such as the Pearson correlation and Euclidean distance. We also compare the CNN depth shifts with depth shifts obtained using the classical cross-correlation method. The CNN performs well and is competitive with cross correlation. CNN performs better for some log types—resistivity, for instance—than others. Several factors influence our results, including the quality of the input data, borehole conditions, pattern differences between LWD and EWL, and significant stretch/squeeze effects. Differences between the mean Pearson correlation computed after CNN and the cross-correlation depth-matching process are of the order of 10–1 and 10–2. Our CNN approach is, therefore, a potential alternative to current depth-matching methods, which may reduce the amount of user intervention required from the petrophysicist.
The oil and gas industry of today is undergoing rapid digitalization. This implies a massive effort to transform standard work procedures and workflows into more efficient practices and implementations using machine learning (ML) and automation. This will enable geoscientists to explore and exploit vast amounts of data quickly and efficiently. To address these current industry challenges, we propose a pilot well-log database in HDF5 (Hierarchical Data Format version 5) format that can be continuously extended if new data become available. It also provides versatility for data preparation for further analysis. We show an alternative way to store and use log files in a hierarchical structure that is easy to understand and handle by research institutes, companies, and academia. We also touch upon well-log depth matching, a long-standing industry challenge, to synchronize data from different logging passes to a single depth reference. Having a robust automated solution for depth matching is important to facilitate the use of all available data in a depth interval for analysis by ML. We propose an automatic well-log depth-matching workflow capable of handling multiple log types simultaneously and its integration with the database. The updated depth-matched logs are added to the database with their corresponding metadata, giving the geoscientist full control. We implemented two algorithms—classical cross correlation combined with a scaling factor to simulate stretch-squeeze effects and a constrained dynamic time warping (DTW). Our results indicate that the classical cross correlation outperforms the warping for both robustness and speed when the DTW is constrained to avoid excessive signal distortion and when the number of processed curves increases, respectively. Some limitations of our approach are related to large changes in the log patterns between the runs, as well as the assumption of negligible depth shift between log types within the same run. The cross correlation also allows a consistent application of depth matching to the metadata. This prototype workflow is tested using two wells from the Norwegian North Sea. We see the potential for extending this automatic database-processing workflow to give geoscientists access to all the data to improve interpretation.
Seismic attenuation distorts phase and narrows bandwidth in seismic surveys. It is also an exploration attribute, as, for example, gas or overpressure, may create attenuation anomalies. Compensating attenuation in imaging requires accurate models. Detailed attenuation models may be obtained using full-waveform inversion (FWI) or attenuation tomography, but their accuracy benefits from reliable starting models and/or constraints. Seismic attenuation and velocity dispersion are necessarily linked for causal linear wave propagation such that higher frequencies travel faster than lower frequencies in an attenuative medium. In publicly released well data from the Norwegian North Sea, we have observed systematic positive linear trends in check-shot drift when comparing (lower frequency) time-depth curves with (higher frequency) integrated sonic transit times. We observe velocity dispersion consistent with layers having constant seismic attenuation. Adapting a previously published method, and assuming an attenuation-dispersion relationship, we use drift gradients, measured over thick stratigraphic units, to estimate interval P-wave attenuation and tentatively interpret its variation in terms of porosity and fluid mobility. Reflectivity modeling predicts a very low attenuation contribution from peg-leg multiples. We use the attenuation values to develop a simple regional relationship between P-wave velocity and attenuation. Observed low drift gradients in some shallower units lead to an arch-shaped model that predicts low attenuation at both low and high velocities. The attenuation estimates were broadly comparable with published effective attenuation values obtained independently nearby. This general methodology for quickly deriving a regional velocity-attenuation relationship could be used anywhere that coincident velocity models are available at seismic and sonic frequencies. Such relationships can be used for fast derivation (from velocities) of starting attenuation models for FWI or tomography, constraining or linking velocity and attenuation in inversion, deriving models for attenuation compensation in time processing, or deriving background trends in screening for attenuation anomalies in exploration.
Petrophysical interpretation and optimal correlation extraction of different measurements require accurate well log depth matching. We have developed a supervised multimodal machine learning alternative for the task of simultaneously matching raw logging while drilling and electrical wireline logging logs. Seven one‐dimensional convolutional neural networks are trained using different log measurements: gamma‐ray, resistivity, P‐ and S‐wave sonic, density, neutron and photoelectric factors, and their depth shift estimates are aggregated using different multimodal late fusion strategies. We test the late fusion average, late fusion weighted average, late fusion with linear and nonlinear learners and model‐level fusion. Depth matching results using the different fusion strategies applied to two unseen wells are compared using visual inspection and the mean Pearson correlation. All models perform well, increasing the correlation after depth matching. Late fusion weighted average achieves the highest scores for all log types. The late fusion weighted average results are compared to a cross‐correlation user‐assisted workflow and manual depth matching for validation. In general, the convolutional neural network fused method exhibits a lower performance than the traditional methods. For one of the wells, the cross‐correlation shows higher correlation values than the other methods but for the second well the manual depth match performs best. However, the differences in Pearson correlation values are small ranging from 0.01 to 0.1. The manual depth match performs very well for the sonic logs, which tend to require slightly larger depth shifts than the other measurements, thus a common depth shift might not always be suitable. Although our convolutional neural network fused approach is limited to estimating bulk shifts and uses constant fusion weights, its performance is similar to that of more time‐consuming methods. Our approach might be substantially improved by including dynamic shifts (stretch/squeeze) and depth‐dependent fusion weights via long‐short‐term memory recurrent neural networks.
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