Prediction of drilling fluid lost-circulation zone based on deep learning

Lost circulation has become a crucial technical problem that restricts the quality and efficiency improvement of the drilling operation in deep oil and gas wells. The lost-circulation zone prediction has always been a hot and difficult research topic on the prevention and control of lost circulation. This study applied machine learning and statistical methods to deeply mine 105 groups and 29 features of loss data from typical loss block M. After removing 10 sets of noise data, the methods of mean removal, range scaling and normalization were used to pre-treat the 95 sets of the loss data. The multi-factor analysis of variance (ANOVA) and random forest algorithm were adopted to determine the 13 main factors affecting the lost circulation. The three typical deep learning neural network models were improved, the parameters in the models were adjusted, the neural network models with different structures were compared according to the PR curves, and the best model structure was built. The pre-treated loss data in 95 sets with 13 features were divided into the training set and test set by a ratio of 4:1. The model performance was evaluated using F1 score, accuracy, and recall rate. The trained model was successfully applied to the G block with severe leakage. The results show that the capsule network model is better than the BP neural network model and the convolutional neural network model. It stabilizes at 300 training rounds, with a prediction accuracy of 94.73%. The improved model can be applied to lost-circulation control in the field and provide guidance on leakage prevention and plugging operations

Data-Driven Modeling and Prediction for Reservoir Characterization and Simulation Using Seismic and Petrophysical Data Analyses

This study explores the application of data-driven modeling and prediction in reservoir characterization and simulation using seismic and petrophysical data analyses. Different aspects of the application of data-driven modeling methods are studied, which include rock facies classification, seismic attribute analyses, petrophysical properties prediction, seismic facies segmentation, and reservoir dimension reduction. The application of using petrophysical well logs to predict rock facies is explored using different data analytics methods including decision tree, random forest, support vector machine and neural network. Different models are trained from a set of well logs and pre-interpreted rock facies data. Among the compared methods, the random forest method has the best performance in classifying rock facies in the dataset. Seismic attribute values from a 3D seismic survey and petrophysical properties from well logs are collected to explore the relationships between seismic data and well logs. In this study, deep learning neural network models are created to establish the relationships. The results show that a deep learning neural network model with multi-hidden layers is capable to predict porosity values using extracted seismic attribute values. The utilization of a set of seismic attributes improves the model performance in predicting porosity values from seismic data. This study also presents a novel deep learning approach to automatically identify salt bodies directly from seismic images. A wavelet convolutional neural network (Wavelet CNN) model, which combines wavelet transformation analyses with a traditional convolutional neural network (CNN), is developed and demonstrated to increase the accuracy in predicting salt boundaries from seismic images. The Wavelet CNN model outperforms the conventional image recognition techniques, providing higher accuracy, to identify salt bodies from seismic images. Besides, this study evaluates the effect of singular value decomposition (SVD) in dimension reduction of permeability fields during reservoir modeling. Reservoir simulation results show that SVD is valid in the parameterization of the permeability field. The reconstructed permeability fields after SVD processing are good approximations of the original permeability values. This study also evaluates the application of SVD on upscaling for reservoir modeling. Different upscaling schemes are applied on the permeability field, and their performance are evaluated using reservoir simulation

Transmissibility Prediction: A Deep Learning Approach

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsTransmissibility is a very important issue in the study of fractured rocks, as it is directly related to the efficiency of drilling and extracting oil wells, water reservoirs, and even gas exploration. In this piece of work, based on data from transmissibility simulations performed in oil fields in Norway, three different machine learning approaches were applied for predicting the transmissibility of fractured rock areas. First, the fracture diagram image was applied in two different Neural Networks architectures: GoogleNet and ResNet. Second, from the fracture diagram image, it was performed a decomposition of all fracture lines (scratches) on each image into X-axis and Y-axis and it was sent to the same two Neural Network architectures on the previous approach (GoogleNet and ResNet). And finally, it was performed a discretizing continuous variable, and applied on neural network ResNet, thus performing a multi-class classification for predictions instead of regression. Overall, this study provides contributions for transmissibility prediction on oil well fields. Creating options to the traditional technique of calculating transmissibility by computer simulation which is very costly and time-consuming

Machine Learning Assisted Framework for Advanced Subsurface Fracture Mapping and Well Interference Quantification

The oil and gas industry has historically spent significant amount of capital to acquire large volumes of analog and digital data often left unused due to lack of digital awareness. It has instead relied on individual expertise and numerical modelling for reservoir development, characterization, and simulation, which is extremely time consuming and expensive and inevitably invites significant human bias and error into the equation. One of the major questions that has significant impact in unconventional reservoir development (e.g., completion design, production, and well spacing optimization), CO2 sequestration in geological formations (e.g., well and reservoir integrity), and engineered geothermal systems (e.g., maximizing the fluid flow and capacity of the wells) is to be able to quantify and map the subsurface natural fracture systems. This needs to be done both locally, i.e., near the wellbore and generally in the scale of the wellpad, or region. In this study, the conventional near wellbore natural fracture mapping techniques is first discussed and integrated with more advanced technologies such as application of fiber optics, specifically Distributed Acoustic Sensing (DAS) and Distributed Strain Sensing (DSS), to upscale the fracture mapping in the region. Next, a physics-based automated machine learning (AutoML) workflow is developed that incorporates the advanced data acquisition system that collects high-resolution drilling acceleration data to infer the near well bore natural fracture intensities. The new AutoML workflow aims to minimize human bias and accelerate the near wellbore natural fracture mapping in real time. The new AutoML workflow shows great promise by reducing the fracture mapping time and cost by 10-fold and producing more accurate, robust, reproducible, and measurable results. Finally, to completely remove human intervention and consequently accelerate the process of fracture mapping while drilling, the application of computer vision and deep learning techniques in new workflows to automate the process of identifying natural fractures and other lithological features using borehole image logs were integrated. Different structures and workflows have been tested and two specific workflows are designed for this purpose. In the first workflow, the fracture footprints on actual acoustic image logs (i.e., full, or partial sigmoidal signatures with a range of amplitude and vertical and horizontal displacement) is detected and classified in different categories with varying success. The second workflow implements the actual amplitude values recorded by the borehole image log and the binary representation of the produced images to detect and quantify the major fractures and beddings. The first workflow is more detailed and capable of identifying different classes of fractures albeit computationally more expensive. The second workflow is faster in detecting the major fractures and beddings. In conclusion, regional subsurface natural fracture mapping technique using an integration of conventional logging, microseismic, and fiber optic data is presented. A new AutoML workflow designed and tested in a Marcellus Shale gas reservoir was used to predict near wellbore fracture intensities using high frequency drilling acceleration data. Two integrated workflows were designed and validated using 3 wells in Marcellus Shale to extract natural fractures from acoustic image logs and amplitude recordings obtained during logging while drilling. The new workflows have: i) minimized human bias in different aspects of fracture mapping from image log analysis to machine learning model selection and hyper parameter optimization; ii) generated and quantified more accurate fracture predictions using different score matrices; iii) decreased the time and cost of the fracture interpretation by tenfold, and iv) presented more robust and reproducible results

Joint Microseismic Event Detection and Location with a Detection Transformer

Microseismic event detection and location are two primary components in microseismic monitoring, which offers us invaluable insights into the subsurface during reservoir stimulation and evolution. Conventional approaches for event detection and location often suffer from manual intervention and/or heavy computation, while current machine learning-assisted approaches typically address detection and location separately; such limitations hinder the potential for real-time microseismic monitoring. We propose an approach to unify event detection and source location into a single framework by adapting a Convolutional Neural Network backbone and an encoder-decoder Transformer with a set-based Hungarian loss, which is applied directly to recorded waveforms. The proposed network is trained on synthetic data simulating multiple microseismic events corresponding to random source locations in the area of suspected microseismic activities. A synthetic test on a 2D profile of the SEAM Time Lapse model illustrates the capability of the proposed method in detecting the events properly and locating them in the subsurface accurately; while, a field test using the Arkoma Basin data further proves its practicability, efficiency, and its potential in paving the way for real-time monitoring of microseismic events

Statistical and deep learning methods for geoscience problems

Machine learning is the new frontier for technology development in geosciences and has developed extremely fast in the past decade. With the increased compute power provided by distributed computing and Graphics Processing Units (GPUs) and their exploitation provided by machine learning (ML) frameworks such as Keras, Pytorch, and Tensorflow, ML algorithms can now solve complex scientific problems. Although powerful, ML algorithms need to be applied to suitable problems conditioned for optimal results. For this reason ML algorithms require not only a deep understanding of the problem but also of the algorithm’s ability. In this dissertation, I show that Simple statistical techniques can often outperform ML-based models if applied correctly. In this dissertation, I show the success of deep learning in addressing two difficult problems. In the first application I use deep learning to auto-detect the leaks in a carbon capture project using pressure field data acquired from the DOE Cranfield site in Mississippi. I use the history of pressure, rates, and cumulative injection volumes to detect leaks as pressure anomaly. I use a different deep learning workflow to forecast high-energy electrons in Earth’s outer radiation belt using in situ measurements of different space weather parameters such as solar wind density and pressure. I focus on predicting electron fluxes of 2 MeV and higher energy and introduce the ensemble of deep learning models to further improve the results as compared to using a single deep learning architecture. I also show an example where a carefully constructed statistical approach, guided by the human interpreter, outperforms deep learning algorithms implemented by others. Here, the goal is to correlate multiple well logs across a survey area in order to map not only the thickness, but also to characterize the behavior of stacked gamma ray parasequence sets. Using tools including maximum likelihood estimation (MLE) and dynamic time warping (DTW) provides a means of generating quantitative maps of upward fining and upward coarsening across the oil field. The ultimate goal is to link such extensive well control with the spectral attribute signature of 3D seismic data volumes to provide a detailed maps of not only the depositional history, but also insight into lateral and vertical variation of mineralogy important to the effective completion of shale resource plays

A seismic-driven 3D model of rock mechanical facies: An example from the Asmari reservoir, SW Iran

Asmari Formation is one of the most prolific and important hydrocarbon reservoirs in Iran. This formation in the Cheshmeh-Khosh oilfield shows mixed carbonate-siliciclastic lithology and its elastic modulus changes are correlatable with facies changes. To address these changes, we investigated the relation between sedimentary environment (facies) and texture with various elastic moduli. The Young's modulus shows higher correlation with the facies changes. Data from three wells are analyzed and used for the construction of rock mechanical facies. Based on elastic properties, facies and texture changes as well as petrophysical characteristics seven rock mechanical facies (RMFs) are recognized in the studied formation. To predict RMFs at inter-well spaces more efficiently and capturing the lateral formation property variationsa 3D rock mechanical facies model is constructed based on seismic attributes. In this method, RMFs are correlatable between the studied wells and mappable by seismic attribute in the field scale. Finally, the distribution of RMFs and their related properties is investigated in the studied field

Case studies in causal inference and anomaly detection

The study of parent-child well interactions in unconventional shales has generated high interest both in the industry and academia over the last decade. This is largely because of the growing number of child wells and their immediate impact on the parent well production owing to several dynamic factors, one of them, including well spacing. Evaluating the impact of well spacing on parent and child well production performance is challenging. Several studies have resorted to geomechanical stress and fracture modeling combined with dynamic simulation techniques while a few operators have chosen field trials to evaluate optimal well spacing. Several data-driven approaches to address the well-spacing problem have also become popular. One such commonly used data-driven approach simply calculates the difference in cumulative production over a specified period of time for parent and child wells grouped by spacing. This approach has been the method of choice for several different recent analyses of well spacing; however, given that the method of simple averages does not account for formation properties or completion design, the results may be compromised and can lead to counterintuitive results. In this thesis, I introduce a new data-driven approach leveraging the power of causal inference as seen in clinical trials for multivariate observational studies. The causal approach addresses the problem behind the routinely used simple averages approach by providing a formalism to control for reservoir and completion variables when evaluating the impact of well spacing on production performance. I apply the causal inference workflow to a dataset from a prolific oil basin in Texas with over 700 wells in the analyses. It includes the formation properties, fluid volume, proppant weight, landing zones and the downhole locations of the wells. Using the causal inference workflow, I evaluate the effect of well spacing on well performance at different parent-child spacing ranges. The optimal well spacing is then estimated for this shale play based on the magnitude of the causal effects. These estimates are then compared with the simple averages approach to demonstrate the power and utility of causality. In the second part of the thesis, I transition into a discussion on anomaly detection approaches applied in the oil and gas industry. I discuss current anomaly detection methods for a widely used artificial lift method – the Sucker Rod Pump (SRP). Today, there is a growing need for fast and accurate anomaly detection systems given the emergence of Internet of Things (IoT) and access to Big Data. Anomaly detection using human operators can be expensive, is often subject to bias and experience-levels and does not scale very well with the need to monitor more than a few tens of wells. With SRPs, the problem of anomaly detection becomes a problem of image classification where dynamometer cards are evaluated for signatures of failure. While this has been the mainstay of anomaly detection for pumpjacks, in this thesis, I automate this task of monitoring and detecting the anomalies from the SRP pump cards. Several thousand synthetic pump cards specific to pump failures modes are generated from the literature and fed to a deep learning model. This deep learning model is a Convolutional Neural Network (CNN) which is commonly used in image classification tasks, speech recognition tasks as well as many other modern-day technology applications including smart phones, self-driving cars, aerospace etc. The CNN used in this work offers a very high accuracy for detecting a variety of pump failures modes thereby offering the potential to save costs, time and unnecessary workovers for the operator