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This research focuses on investigating the relative performance of a range of machine learning algorithms, namely the artificial neural network, support vector machine, Gaussian process regression, random forest, and XGBoost, for predicting the undrained shear strength from cone penetration test data. This is to assess how machine learning could help us lower the need for laboratory test data. The training dataset compiles 526 data from 12 regions and the testing dataset consists of 20 data from a polder located close to Leiden in the Netherlands. In addition, k-fold and group k-fold cross-validation strategies are both applied to validate the models. The poor performance of the models during group k-fold cross-validation suggests that, while machine learning techniques can perform well when site-specific data are included during training, they struggle to generalize without site-specific data. This highlights the difficulty of capturing soil heterogeneity and suggests that either machine learning methods should be trained on specific sites for which some data are already available, or much larger training datasets are needed., Geo-engineering, Applied Geology

Many stratigraphic features occur at a scale that is at the edge or below vertical seismic resolution. Thus, they cannot be directly observed in the seismic data, while still having an important effect on the fluid flow within the system. The better understanding of these sub-seismic scale features or heterogeneities can help decrease subsurface uncertainty. Here we present a novel method that integrates forward stratigraphic modelling, petrophysics, and geophysics to decipher the seismic imprint of heterogeneities in wave-dominated, shallow marine environments. The proposed three-stepped method starts with defining geology-related input parameters for BarSim, a stratigraphic forward modelling software that produces models that include stratigraphic architecture, grain size distribution, and facies distribution. Then, the geological data is translated, cell by cell, into petrophysical data (density, Vp, and Vs) using emphirical relationships. Finally, the forward seismic modelling is performed by combining a finite difference approach strategy and angle-dependent full wavefield migration to retrieve the angle gathers This method also allows the generation of large amounts of field-independent data suitable for machine learning applications., Applied Geology, Applied Geophysics and Petrophysics

Understanding the impact of fractures on fluid flow is fundamental for developing geoenergy reservoirs. Pressure transient analysis could play a key role for fracture characterization purposes if better links can be established between the pressure derivative responses (p′) and the fracture properties. However, pressure transient analysis is particularly challenging in the presence of fractures because they can manifest themselves in many different p′ curves. In this work, we aim to provide a proof-of-concept machine learning approach that allows us to effectively handle the diversity in fracture-related p′ curves by automatically classifying them and identifying the characteristic fracture patterns. We created a synthetic dataset from numerical simulation that comprised 2560 p′ curves that represent a wide range of fracture network properties. We developed an unsupervised machine learning approach that can distinguish the temporal variations in the p′ curves by combining dynamic time warping with k-medoids clustering. Our results suggest that the approach is effective at recognizing similar shapes in the p′ curves if the second pressure derivatives are used as the classification variable. Our analysis indicated that 12 clusters were appropriate to describe the full collection of p′ curves in this particular dataset. The classification exercise also allowed us to identify the key geological features that influence the p′ curves in this particular dataset, namely (1) the distance from the wellbore to the closest fracture(s), (2) the local/global fracture connectivity, and (3) the local/global fracture intensity. With additional training data to account for a broader range of fracture network properties, the proposed classification method could be expanded to other naturally fractured reservoirs and eventually serve as an interpretation framework for understanding how complex fracture network properties impact pressure transient behaviour., Applied Geology

Simulating realistic sedimentary bodies while conditioning all the available data is a major topic of research. We present a new method to simulate the channel morphologies resulting from the deposition processes. It relies on a formal grammar system, the Lindenmayer system, or L-system. The L-system puts together channel segments based on user-defined rules and parameters. The succession of segments is then interpreted to generate non-rational uniform B-splines representing straight to meandering channels. Constraints attract or repulse the channel from the data during the channel development. They enable to condition various data types, from well data to probability cubes or a confinement. The application to a synthetic case highlights the method's ability to manage various data while preserving at best the channel morphology.

Turbiditic channels evolve continuously in relation to erosion-deposition events. They are often gathered into complexes and display various stacking patterns. These patterns have a direct impact on the connectivity of sand-rich deposits. Being able to reproduce them in stochastic simulations is thus of significant importance. We propose a geometrical and descriptive approach to stochastically control the channel stacking patterns. This approach relies on the simulation of an initial channel using a Lindenmayer system. This system migrates proportionally to a migration factor through either a forward or a backward migration process. The migration factor is simulated using a sequential Gaussian simulation or a multiple-point simulation. Avulsions are performed using a Lindenmayer system, similarly to the initial channel simulation. This method makes it possible to control the connectivity between the channels by adjusting the geometry of the migrating areas. It furnishes encouraging results with both forward and backward migration processes, even if some aspects such as data conditioning still need to be explored.

Very different connectivity patterns may arise from using different simulation methods or sets of pa-rameters, and therefore different flow properties. This paper proposes a systematic method to compare ensemble of categorical simulations from a static connectivity point of view. The differences of static con-nectivity cannot always be distinguished using two point statistics. In addition, multiple-point histograms only provide a statistical comparison of patterns regardless of the connectivity. Thus, we propose to char-acterize the static connectivity from a set of 12 indicators based on the connected components of the realizations. Some indicators describe the spatial repartition of the connected components, others their global shape or their topology through the component skeletons. We also gather all the indicators into dissimilarity values to easily compare hundreds of realizations. Heat maps and multidimensional scal-ing then facilitate the dissimilarity analysis. The application to a synthetic case highlights the impact of the grid size on the connectivity and the indicators. Such impact disappears when comparing samples of the realizations with the same sizes. The method is then able to rank realizations from a referring model based on their static connectivity. This application also gives rise to more practical advices. The multidimensional scaling appears as a powerful visualization tool, but it also induces dissimilarity mis-representations: it should always be interpreted cautiously with a look at the point position confidence. The heat map displays the real dissimilarities and is more appropriate for a detailed analysis. The com-parison with a multiple-point histogram method shows the benefit of the connected components: the large-scale connectivity seems better characterized by our indicators, especially the skeleton indicators.

Simulating realistic sedimentary bodies while conditioning all the available data is a major topic of research. We present a new method to simulate the channel morphologies resulting from the deposition processes. It relies on a formal grammar system, the Lindenmayer system, or L-system. The L-system puts together channel segments based on user-defined rules and parameters. The succession of segments is then interpreted to generate non-rational uniform B-splines representing straight to meandering channels. Constraints attract or repulse the channel from the data during the channel development. They enable to condition various data types, from well data to probability cubes or a confinement. The application to a synthetic case highlights the method's ability to manage various data while preserving at best the channel morphology.

Turbiditic channels evolve continuously in relation to erosion-deposition events. They are often gathered into complexes and display various stacking patterns. These patterns have a direct impact on the connectivity of sand-rich deposits. Being able to reproduce them in stochastic simulations is thus of significant importance. We propose a geometrical and descriptive approach to stochastically control the channel stacking patterns. This approach relies on the simulation of an initial channel using a Lindenmayer system. This system migrates proportionally to a migration factor through either a forward or a backward migration process. The migration factor is simulated using a sequential Gaussian simulation or a multiple-point simulation. Avulsions are performed using a Lindenmayer system, similarly to the initial channel simulation. This method makes it possible to control the connectivity between the channels by adjusting the geometry of the migrating areas. It furnishes encouraging results with both forward and backward migration processes, even if some aspects such as data conditioning still need to be explored.

Very different connectivity patterns may arise from using different simulation methods or sets of pa-rameters, and therefore different flow properties. This paper proposes a systematic method to compare ensemble of categorical simulations from a static connectivity point of view. The differences of static con-nectivity cannot always be distinguished using two point statistics. In addition, multiple-point histograms only provide a statistical comparison of patterns regardless of the connectivity. Thus, we propose to char-acterize the static connectivity from a set of 12 indicators based on the connected components of the realizations. Some indicators describe the spatial repartition of the connected components, others their global shape or their topology through the component skeletons. We also gather all the indicators into dissimilarity values to easily compare hundreds of realizations. Heat maps and multidimensional scal-ing then facilitate the dissimilarity analysis. The application to a synthetic case highlights the impact of the grid size on the connectivity and the indicators. Such impact disappears when comparing samples of the realizations with the same sizes. The method is then able to rank realizations from a referring model based on their static connectivity. This application also gives rise to more practical advices. The multidimensional scaling appears as a powerful visualization tool, but it also induces dissimilarity mis-representations: it should always be interpreted cautiously with a look at the point position confidence. The heat map displays the real dissimilarities and is more appropriate for a detailed analysis. The com-parison with a multiple-point histogram method shows the benefit of the connected components: the large-scale connectivity seems better characterized by our indicators, especially the skeleton indicators.