Coffee Break

Poster Spotlight

*Mauricio Araya-Polo, Stuart Farris and Manuel Florez, Combining Unsupervised and Supervised Deep Learning approaches for Seismic Tomography Signals from inner earth, seismic waveforms, are heavily manipulated before human interpreters have a chance of figuring the subsurface structures. That manipulation adds modeling biases and it is limited by methodological shortcomings. Alternatively, using waveforms directly is becoming possible thanks to current Deep Learning (DL) advances such as (Araya-Polo et al., 2017 and 2018; Lin et al., 2017). Further extending that work, we present a DL approach that takes realistic raw seismic waveforms as inputs and produces subsurface velocity models as output. When insufficient data is used for training, DL algorithms tend to either over-fit or fail completely. Gathering large amounts of labeled and standardized seismic data sets is not straight forward. We address this shortage of quality data by building a Generative Adversarial Network (GAN) to augment our original training data set, which then is used by the DL seismic tomography as input.

*Yuxing Ben, Chris James, Dingzhou Can, Drilling State Classification with Machine Learning The sensors on drilling rigs and production sites are leading oil and gas companies to mine so-called big data. Leveraging historical time series data and real-time drilling data can help drilling engineers improve rig and well delivery efficiencies; however, it can also help geoscientists understand the geophysical properties of the reservoir. In this case study, we describe how to use machine learning to classify drilling states. We investigated several machine learning methods and architectures including Random Forest tree models, Convolutional Neural Networks, and Recurrent Neural Networks which were then tested against 15 million rows of real, labeled drilling time-series data. We found that machine learning models were superior to rule based models. For wells drilled in two different onshore basins, the accuracies of our in-house rule based models were 70% and 90% respectively, while the accuracies of machine learning models were over 99%. The best identified machine learning model has been deployed in a drilling analytics platform and used to automatically detect the drilling state in realtime for use by Drilling Engineers to evaluate and analyze well performance.

*Jorge Guevara, Blanca Zadrozny, Alvaro Buoro, Ligang Lu, John Tolle, Jan Limbeck, Mingqi Wu, Defletf Hohl, An Interpretable Machine Learning Methodology for Well Data Integration and Sweet Spotting Identification. The huge amount of heterogeneous data provided by the petroleum industry brings opportunities and challenges for applying machine learning methodologies. For instance, petrophysical data recorded in well logs, completions datasets and well production data also constitute good examples of data for training machine learning models with the aim of automating procedures and giving data-driven solutions to problems arisen in the petroleum industry. In this work, we present a machine learning methodology for oil exploration that 1) opens the possibility of integration of heterogeneous data such as completion, engineering, and well production data, as well as, petrophysical feature estimation from petrophysical data from horizontal and vertical wells; 2) it enables the discovery of new locations with high potential for production by using predictive modeling for sweet spotting identification; 3) it facilitates the analysis of the effect, role, and impact of some engineering decisions on production by means of interpretable Machine learning modeling, allowing the model validation; 4) it allows the incorporation of prior/expert knowledge by using Shape Constraint Additive Models and; 5) it enables the construction of hypothetical "what-if" scenarios for production prediction. Among the results, it is important to highlight that 1) performance improves by including prior knowledge via SCAMs, for example, we have a percentage change of 24% between the best RMSE result from black-box ML models vs a model that incorporates prior knowledge. 2) we were able to construct hypothetical what-if scenarios based on actual petrophysical data and hypothetical completion and engineering values, 3) we were able to assess the validity of ML models through effect analysis via conditonal plots.

*Ping Lu, Hunter Danque, Jianxiong Chen, Seth Brazell, and Mostafa Karimi, Enhanced Seismic Imaging with Predictive Neural Networks for Geophysics Full-waveform inversion (FWI) has become a popular method to estimate elastic earth properties from seismic data, and it has great utility in seismic velocity model building and seismic reflectivity imaging in areas of complex salt. FWI is a non-linear data-fitting procedure that matches the predicted to observed waveform data given an initial guess of the subsurface parameters. The velocity model parameters are updated to reduce the misfit between the observed and predicted data until the misfit is sufficiently small. Sharp velocity boundaries such as between salt and sediment are often updated manually for each iteration based on the seismic reflectivity images. Here, we propose a predictive neural network architecture as a potential alternative to the complex FWI workflow. An unsupervised learning model of predicting of future frames in a video sequence is explored to simulate direct inversion procedures for seismic data. Such neural network architectures are comprised of two main components: an encoder based on convolutional neural networks (CNNs), and a recurrent neural networks (RNNs) for iteratively predicting geophysical velocity models. Both the proposed networks are able to robustly train individual layers and make a layer-specific prediction, which is compared with a target to produce an error term. It is then propagated to the subsequent network layers. With a few iterative training steps, the networks are capable of learning internal representations decoded from latent parameters of seismic wave propagation which controls how FWI velocity modelling converges. These representations learned from one dataset could be transferred to predict the future velocity model of a brand-new area where the shape of salt body is not well imaged or known. Altogether, experimental results generated from a real Gulf of Mexico seismic data suggest that the prediction represents a powerful framework for unsupervised learning, which provides an alternative approach to the FWI procedure to generate a high resolution velocity model including an accurate salt model and ultimately a sharp subsalt image.

*Zachary Ross, PhaseLink: A Deep Learning Approach to Seismic Phase Association We present PhaseLink, a deep learning approach to seismic phase association. Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. Our PhaseLink approach has many desirable properties. First, it is trained entirely on synthetic simulated data (i.e., "sim-to-real"), and is thus easily portable to any tectonic regime. Second, it is straightforward to tune PhaseLink by simply adding examples of problematic cases to the training dataset -- whereas conventional approaches require laborious adjusting of ad hoc hyperparameters. Third, we empirically demonstrate state-of-the-art performance in a wide range of settings. For instance, PhaseLink can precisely associate P- and S-picks to events that are separated by ~12 seconds in origin time. We expect PhaseLink to substantially improve many aspects of seismic analysis, including the resolution of seismicity catalogs, real-time seismic monitoring, and streamlined processing of large seismic datasets.

*Timothy Draelos, Stephen Heck, Jennifer Galasso, and Ronald Brogan, Seismic Phase Identification with a Merged Deep Neural Network Seismic signals are composed of the seismic waves (phases) that reach a sensor, similar to the way speech signals are composed of phonemes that reach a listener’s ear. We leverage ideas from speech recognition for the classification of seismic phases at a seismic sensor. Seismic Phase ID is challenging due to the varying paths and distances an event takes to reach a sensor, but there is consistent structure and ordering of the different phases arriving at the sensor. Together with scalar value measurements of seismic signal detections (horizontal slowness, amplitude, Signal-to-Noise Ratio (SNR), and the time since the previous signal detection), we use the seismogram and its spectrogram of detection waveforms as inputs to a merged deep neural network (DNN) with convolutional (CNN) and recurrent (LSTM) layers to learn the frequency structure over time of different phases. The binary classification performance of First-P phases versus non-First-P (95.6% class average accuracy) suggests a potentially significant impact on the reduction of false and missed events in seismic signal processing pipelines. Other applications include discrimination between noise and non-noise detections for induced seismicity networks and for early warning of large hazards

*Ben Moseley, Andrew Markham, and Tarje Nissen-Meyer, Fast Approximate Simulation of Seismic Waves with Deep Learning The simulation of seismic waves is a core task in many geophysical applications, yet it is computationally expensive. As an alternative approach, we simulate acoustic waves in horizontally layered media using a deep neural network. In contrast to traditional finite-difference (FD) modelling, our network is able to directly approximate the recorded seismic response at multiple receiver locations in a single inference step, without needing to iteratively model the seismic wavefield through time. This results in an order of magnitude reduction in simulation time, from the order of 1 s for FD modelling to the order of 0.1 s using our approach. Such a speed improvement could lead to real-time seismic simulation applications and benefit seismic inversion algorithms based on forward modelling, such as full waveform inversion. Our network design is inspired by the WaveNet network originally used for speech synthesis. We train our network using 50,000 synthetic examples of seismic waves propagating through different horizontally layered velocity models. We are also able to alter our WaveNet architecture to carry out seismic inversion directly on the dataset, which offers a fast inversion algorithm.

• Men-Andrin Meier, Zachary Ross, Anshul Ramachandran, Ashwin Balakrishna, Suraj Nair, Peter Kundzicz, Zefeng Li, Egill Hauksson, Jennifer Andrews, Reliable Real-Time Signal/Noise Discrimination with Deep and Shallow Machine Learning Classifiers In Earthquake Early Warning (EEW), every sufficiently impulsive signal is potentially the first evidence for an unfolding large earthquake. More often than not, however, impulsive signals are mere nuisance signals. One of the most fundamental - and difficult - tasks in EEW is to rapidly and reliably discriminate between real local earthquake signals, and any kind of other signal. Current EEW systems struggle to avoid discrimination errors, and suffer from false and missed alerts. In this study we show how machine learning classifiers can strongly improve real-time signal/noise discrimination. We develop and compare a series of non-linear classifiers with variable architecture depths, including random forests, fully connected, convolutional (CNN, Figure 1) and recurrent neural networks, and a generative adversarial network (GAN). We train all classifiers on the same waveform data set that includes 374k 3-component local earthquake records with magnitudes M3.0-9.1, and 946k impulsive noise signals. We find that the deep architectures significantly outperform the more simple ones. Using 3s long waveform snippets, the CNN and the GAN classifiers both reach 99.5% precision and 99.3% recall on an independent validation data set. Our results suggest that machine learning classifiers can strongly improve the reliability and speed of EEW alerts.

*Mathieu Chambefort, Nicolas Salaun, Emillie Chautru, Stephan Clemencon, Guillaume Poulain, Signal and Noise Detection using Recurrent Autoencoders on Seismic Marine Data In order to meet the industrial constraints in the Big Data era, i.e. processing more and more seismic data (more than 106 shot points per marine seismic survey from [Belz and Dolymnyj, 2018]) in a more timely, reliable and efficient manner (i.e. with a better signal enhancement, [Martin et al., 2015]), we develop a deep learning approach based on recurrent LSTM ([Wong and Luo, 2018]) to the processing of seismic time series, so as to separate the signal from the noise based on the encoded information. This contribution provides empirical evidence that the representation provided by the internal layers of the autoencoder deployed encodes well the original information. More precisely, focus is here on the linear noise possibly blurring marine seismic data, which is mainly due to the tug and motor of the boat but can also be caused by bad weather or other elements, rig and other boats in the area ([Elboth et al., 2009]). The data under study are composed of massive synthetic shot points. The goal pursued is to design an autoencoder capable of detecting the possible occurrence of linear noise in the data. The encoded information is next classified and the results obtained are compared with those of a traditional technique, that essentially consists in applying directly a K -NN algorithm on the envelope of the analytical signal, as if all the dataset comes from the same area.

*Xiaojin Tan and Eldad Haber, Semantic Segmentation for Geophysical Data Segmentation of geophysical data is the process of dividing a geophysical image into multiple geological units. This process is typically done manually by experts, it is time consuming and inefficient. In recent years, machine learning techniques such as Convolutional Neural Networks (CNNs) have been used for semantic segmentation. Semantic segmentation is the process that associates each pixel in a natural image with a labeled class. When attempting to use similar technology to automatically segment geophysical data there are a number of challenges to consider, in particular, data inconsistency, scarcity and complexity. To overcome these challenges, we develop a new process that we call geophysical semantic segmentation (GSS). This process addresses the pre-processing of geophysical data in order to enable learning, the enrichment of the data set (data augmentation) by using a geo-statistical technique, referred to as Multiple-Point Simulations (MPS) and finally, the training of such a data set based on a new neural network architecture called inverse Convolution Neural Networks (iCNN) that is specifically developed to identify patterns. As demonstrated by the results on a field magnetic data set, this approach shows its competitiveness with human segmentation and indicates promising results.

*B Ravi Kiran and Stefan Milz, Aerial LiDAR reconstruction using Conditional GANS Recently, aerial LiDAR data opened lots of new opportunities for many research disciplines like macroscopic geophysical analysis or archaeological investigations. However, LiDAR measurements are expensive and the data is not widely distributed or accessible. We propose a novel method for image to image translation performing HD-LiDAR reconstruction using RGB input images based on conditional GANs. The conditional mapping function of the generator G : [c; z] -> y is transformed to G : [x; z] -> y , whereas y represents the reconstructed LiDAR map and c represents the condition. c is replaced by the aligned aerial camera image x . z represents the noise. Our approach is able to reconstruct LiDAR data as elevation maps based on small scaled training data, which includes RGB and LiDAR sample pairs based on 256  256 image matrices. The model offers the opportunity to complete geophysical LiDAR databases, where measurements are missing. The method is validated on the ISPRS dataset with an overall rRMSE of 14.53% .

Zheng Zhou, Youzuo Lin, Zhongping Zhang, Zan Wang, Robert Dilmore and George Guthrie, CO2 and Brine Leakage Detection Using Multi-Physics-Informed Convolutional Neural Networks In carbon capture and sequestration, it is crucial to build effective monitoring techniques to detect both brine and CO2 leakage from legacy wells into underground sources of drinking water. The CO2 and brine leakage detection methods rely on geophysical observations from different physical domains. Most of the current detection methods are built on physical models, and the leakage mass of CO2 and brine are detected separately. However, those physics-driven methods can be computationally demanding and yields low detection accuracy. In this paper, we developed a novel end-to-end data-driven detection method, called multi-physics-informed convolutional neural network (Multi-physics CNN), which directly learns a mapping relationship between physical measurements and leakage mass. Our Multi-physical CNN takes simulated reflection seismic and pressure data as inputs, and captures different patterns in leakage process. In particular, we capture two types of multi-physical features from seismic and pressure data, respectively. With those features, we can further detect the CO2 and brine leakage mass, simultaneously. We evaluate our novel method for CO2 and brine leakage mass detection task on simulated multi-physical datasets generated using Kimberlina 1.2 model. Our results show that our Multi-physics CNN yields promising results in detecting both leakage mass of CO2 and brine.