- Home
- Conferences
- Conference Proceedings
- Conferences
First EAGE Digitalization Conference and Exhibition
- Conference date: November 30 - December 3, 2020
- Location: Vienna, Austria
- Published: 30 November 2020
21 - 40 of 93 results
-
-
Automatic Method for Anomaly Detection while Drilling
Authors M. Golitsyna, A. Semenikhin, I. Chebuniaev, V. Vasilyev, V. Koryabkin, V. Makarov, I. Simon, T. Baybolov and O. OsmonalievaSummaryA lot of anomalies can occur and lead to failures during drilling process. It is crucial to detect these deviations from normal process as soon as possible, so engineers can analyse and decide what activities to take in order to prevent potential NPT.
In this work we propose a new machine learning based approach for detection abnormal drilling behaviour in an online manner. The idea is to cluster drilling data, which is preprocessed in a very special way. Our aproach allows using all available data for training as it does not need any labeled data and incorporates both raw drilling parameters and expert knowledge, thus enhancing prediction results.
-
-
-
Uncover 2% Advanced Production Optimization across Complex Operational Plants through Industry 4.0, AI and Digital Twin
Authors D. Piotrowski and J. KalagnnanamSummaryThis is a client case study illustrating an advanced implementation of Industry 4.0, AI, and digital twin to achieve material gain in production optimization across complex, interdependent plant processes. From working with leadership and selecting a the right impactful business case to implementation and garnering support from operational stakeholders, we demonstrate how end-to-end value chain optimization is possible.
-
-
-
Production Optimization Under Constraints: Development and Application of Software Combining Data Science and Petroleum Engineering Knowledge
By G. JoffreSummaryOMV New Zealand gas/condensate fields’ gas production is limited by commercial demand, which also constrain production of associated condensate. No test separators nor individual well multiphase flow meters are installed, only single-phase gas flow meters (V-cones flow meters and orifice plate) for each individual well. In order to produce the maximum revenue for the fields, the wells with the highest condensate-gas-ratio need to be prioritized, while still ensuring that well and facilities constraints are managed.
An agile crew of engineers, developers and data scientists, have been mobilized to design and create reliable, easy to use and easy to maintain software solutions to solve three different parts of the optimization problem: A live, dynamic visualization of the wells operating envelopes for dynamic monitoring of the current status of individual wells versus the constraints and direct comparison with simulation models results. A software solution to automatically identify step-changes in well gas, water and condensate rates at facility output level, using these changes to improve CGR and WGR allocated value for each individual well. A software application to calculate the best combination of individual well rates to meet gas export demand while maximizing condensate production, within facility limits and well operating envelopes.
-
-
-
Data-Driven Detection of Well Events in Mature Gas Fields
Authors J. Poort, P. Shoeibi Omrani and A.L. VecchiaSummaryThe production optimization of mature gas fields is severely complicated by the occurrence of certain undesired well events such as salt precipitation, liquid loading, or gas/water coning. Learning from production data of periods in which such events have taken place could help operators improve the process optimization. However, due to the current manual process of interpreting production data, many well events can go unreported. Reanalyzing historic data could retrieve missed events, but this is a time-consuming and costly process. In this study, the dynamic time warping (DTW) algorithm was used in a developed workflow that automates the process of detecting well events which can be operational both in an offline and real-time manner. Such a workflow supports operators in finding well events within production data based on characteristics of target events provided by operators. Based on a case study using field data for a gas well suffering from salt precipitation, the workflow has been proven to be accurate and significantly computational-efficient in finding 8 new events which were not detected by the operator. Additionally, the algorithm was robust in detecting well events even after introducing up to 10% of added noise.
-
-
-
Automated Surface Fault Block Delineation
By T. BrennaSummaryWe present a fully automated method for delineating potential compartments in faulted reservoirs based solely on the geometry of a single reservoir horizon interpretation. Such a technology has potential applications in for instance reservoir compartmentalization studies where it is often advantageous to have an a priori delineation of the reservoir compartments as a credible starting point for the analysis.
In our solution we integrate methods from the geometric modeling discipline, for extracting high-quality curvature information, and novel extensions of existing image processing techniques for segmentation. The result is a fully transparent, deterministic and extensible workflow.
Getting automation right will create value in itself by freeing domain experts from manual laborious work to focus on more fulfilling, higher-value activities. Also, automation could be an enabler for entirely new intelligent, or even transformational, workflows by effectively letting us bypass processes requiring manual user interaction to ultimately leverage alternative applications of the technology stacks. The impact of automation in the emerging digital space will empower us with new capabilities enabling accelerated hydrocarbon discovery.
-
-
-
Deep Bayesian Neural Networks for Fault Identification and Uncertainty Quantification
Authors L. Mosser, S. Purves and E.Z. NaeiniSummaryThe interpretation of faults within a geological basin or reservoir from seismic data is a time-consuming, and often manual task associated with high uncertainties. Recently, numerous approaches using machine learning, especially various types of convolutional neural networks, have been presented to automate the process of identifying fault planes within seismic images, which have been shown to outperform traditional fault detection techniques. While these proposed methods show good performance, many of these approaches do not allow investigation of the associated uncertainties that arise in the fault identification process. In this study, we present an application of Bayesian deep convolutional neural networks for identifying faults within seismic datasets. Using an approximate Bayesian inference method a Bayesian deep neural network was trained on a large dataset of synthetic faulted seismic images. The model is then applied to a benchmark dataset and a real data case from NW shelf Australia to identify fault planes, and to investigate the associated uncertainty in the predictive distribution.
-
-
-
Processing Thin Section Photos with Neural Networks and Computer Vision
Authors S. Polushkin, Y. Volokitin, I. Edelman, E. Sadykhov, O. Lokhanova, Y. Murzaev and S. PastushkovSummaryThe neural network, which was designed for diagnosing cardiovascular diseases was trained to identify and analyze grains at thin section photos. Identifying pores and pore throats is done with computer vision. About 150 thin section photos were processed in about 20 minutes. The output contains grain sizes and mineral composition for more than 10000 grains, and pores and pore throat diameters for several thousand of pores. Comparison with alternative methods of determining pore size distribution like Cap Curves and NMR is presented.
-
-
-
CESI Is a Numerical Approach for Oil Field Study Optimization
Authors O. Melnikova, B. Belozerov and I. PavelevaSummaryThe main goals of oil field study are risks reduction during appraisal and exploration stage and uncertainty decreasing during exploration and development stages.
Software module (patent name is KOGI, English equivalent of this abbreviation is CESI), which based on methodology of complex exploration state estimation (CESI), is a digital tool for identifying zones of insufficient researches. Consequently, these are zones of high risk and uncertainty in terms of STOIIP calculation and planning future investigations.
Methodology is still developing and it is in process adding qualitative features of productive formations (such as complexity, architecture aspects etc.) as well as value of information (VOI) getting from studies.
-
-
-
Digital Multiscale Flow Modeling for Fractured Carbonates with Hessian-Based Cracks Detection
Authors I. Varfolomeev, N. Evseev, O. Ridzel, V. Abashkin, A. Zozulya, S. Karpukhin and M. MiletskySummaryThe results of the pilot project on studying petrophysical and transport properties of core from a fractured carbonate gas-condensate reservoir are described. The studied whole core samples are characterized by low absolute permeability of matrix and highly heterogeneous multiscale network of cracks and fractures. Modern full core 3D X-Ray computed tomography was unable to resolve the geometry of thinner cracks, which made it impossible to create a regular binary solid/void digital rock model typically used for pore-scale hydrodynamic modeling. Thus, a Hessian-based crack detection method, allowing to differentiate voxels with different permeabilities, was employed to construct a model with effective properties. To calibrate the effective properties, smaller sub-plugs were scanned at substantially higher resolution and their images were spatially registered to the whole-core image. The density functional hydrodynamics + chemical potential drive method was used to carry out numerical simulation of three-phase water-gas-condensate flow on the constructed whole core digital rock model with effective properties.
-
-
-
Smarter Well Engineering Concepts Aid in Reducing Planning Time and Increasing ROP
Authors N. Islam, A. Rosener, W. Souza and M. YasirSummaryModern well engineers struggle with digital confusion; they have either too much data or not enough, and the quality is often questionable. Additionally, well engineers are usually operations focused and might not fully appreciate optimization through data-driven insight. This paper illustrates how to optimize the rate of penetration (ROP) in any given field using an automated and timesaving process for designing wells using machine-learning (ML) techniques.
By prescribing optimized ROPs through automated ML of offset well attributes, free from subjective human bias, engineers can push technical limits. Automated analysis, regression, and visualization of high-volume data can reduce planning time significantly and help establish optimized operational parameters to reduce drilling time and costs.
The next step is to build a real-time downhole advisory system to help achieve the predicted ROPs by predicting and prescribing drilling parameters ahead of the bit.
-
-
-
Solving Problems with the Discrete Smooth Interpolation Framework, from Geomodelling to Geophysics and Beyond
Authors A. Tertois and Z. KorenSummaryA number of algorithms developed in geomodelling software rely on the Discrete Smooth Interpolation (DSI) method, a mathematical framework which enables interpolation of sparse values with geological and geophysical constraints on any type of discrete models such as triangulated surfaces or volumetric grids. Leaning perhaps more towards data integration than machine learning, this powerful tool is also evolving as part of our digital transformation. Today’s dynamic environment is favourable to building upon DSI’s principles and ability to add geological or physical concepts as constraints in discrete models.
DSI already offers solutions to many geomodelling problems as part of a successful commercial software suite. The Fourth Industrial Revolution is an opportunity to rejuvenate DSI by lifting it out of the geomodelling toolkit and making it available as a separate entity for any scientist to use, as a seamless and invisible link between linear equations and elegant solutions.
In this paper, we first review the Discrete Smooth Interpolation theory, then show how we currently apply it to various geomodelling problems and finally, we look towards its future in helping us solve our digital challenges in different domains.
-
-
-
Multi-Sensor Acoustic Parameter Analysis System for Monitoring, and Performance Prediction of Deep Drilling and Stimulation Operations
More LessSummaryAcoustic Emission (AE) based systems have been under development and used at Fraunhofer IEG to monitor, evaluate, and control conventional and novel drilling processes and their pertinent equipment used in geothermal and drilling applications. Moreover, novel jetting and drilling operations in deep geothermal reservoirs do heavily rely on such new technologies in order to be able to control them properly and thus, to result in a viable technical and economical option.
AE monitoring is based on the detection and conversion of elastic waves into electrical signals, which are associated with a rapid release of localized stress-energy propagating within a material. It is passive testing, logging, and analysis method to evaluate changes in the properties and behavior of machines and mineral type materials such as rocks. Such changes may be induced by drilling, jetting, or other drilling methods and being recorded, characterized, and evaluated via an AE system and will be used ultimately used for process performance prediction using machine learning methods. This is the core of the novel monitoring system development, the AE based, so-called Multi-Sensor acoustic parameter analysis as the primary control and monitoring mechanism during rock breaking, drilling, jetting, and stimulation.
-
-
-
Standardized Direct Data Transfers Between Applications Accelerates Workflows and Improves Operational Adoption of Innovative Technologie
More LessSummaryGeoscience and engineering workflows are applied to increasingly complex reservoirs. Collaborative teams require the use of different vendor solutions to apply the best technologies to solve problems and deliver the most accurate models and predictions. A new direct data transfer protocol based on existing mature industry standards simplifies and speeds up the data connection between applications. It also ensures better data integrity and complete flexibility in assembling and executing workflows. Based on the mature WebSockets protocol, this new standard has the necessary sub-protocols to reliably handle complex data relationships, very large data arrays as well as unique item identifiers. In addition to accelerating workflows and making them more reliable, this new protocol simplifies the addition of new innovative technologies alongside proven ones, for the best outcome within the tight resource and time limits imposed by the upstream industry.
-
-
-
A Digital Methodology for Large Scale Integrated Optimization of Production Planning and Operations
By M. ScottSummaryProducing assets and their gathering networks are multi-faceted, with multiple diverse data sets and modelling and analysis tools. Consolidating these into a single automated, operational environment can greatly streamline surveillance and management of these assets. However, this type of modelling does not always properly represent the asset as a whole, as individual elements have impacts on preceding and subsequent areas. The purpose of this presentation is to show a simple and effective methodology to generate this integrated model, to allow optimization of production planning and operation processes. By leveraging modern data integration, modelling and orchestration tools, up to date insight into all aspects of the operation can be provided across the business, enhancing planning, forecasting and decision making capabilities.
-
-
-
Accelerating Seismic Data Access, QC and Vendor-Independent Automated Workflows with Cloud-Based Seismic Datastore and API
Authors C. Caso, P. Aursand and T. StraySummarySeismic data discovery, quality assessment, and retrieval are often time-consuming and iterative processes between geoscientists and data managers.
In this paper, we describe the implementation of a seismic datastore in the Aker BP cloud environment, as part of the company’s digital program Eureka. The objectives of this implementation have been to get fast, tool-independent access to the Aker BP seismic data through an API allowing queries of the whole survey but also of subsets of the seismic data; overview of the actual data within each survey; preview of a seismic section (inline, crossline or arbitrary line); comparison of the same section from two different seismic cubes; and enabling 3rd party applications to run as services on top of the seismic. The implementation was carried out in a five-month project, involving software developers and the support of data managers, in an Agile setup with demos every two weeks and continuous feedback from the end-users. The solution has been delivered as a cloud-based API architecture to ingest, store, query, visualize and consume seismic data.
-
-
-
Facies Classification: Combining Domain Knowledge with Machine Learning Solutions
SummaryAutomated facies identification workflows which use Machine Learning (ML) are publicly available but perform sub-optimally (accuracy in the order of 60%) due to a lack of integration with geological domain knowledge. Existing tools consider well log values mostly on a depth-by-depth basis, using only very basic feature engineering. Our solution aims to integrate ML with well-established geoscience principles (also referred to as geo-rules) such as sequence stratigraphy, proximal-distal trends, and log-trend patterns. Geological knowledge is incorporated into ML to improve the quality and robustness of facies prediction and is captured as additional geologically-inspired features added to the dataset. These features include the mean value and other derived properties of intervals, density-neutron separation, segmentation and wavelet transform. All ML algorithms tested with this augmented set of features show significant improvement in performance metrics as compared to solutions with basic logs only.
-
-
-
Deep Learning Applications to Unstructured Geological Data: From Rock Images Characterization to Scientific Literature Mining
Authors A. Bouziat, S. Desroziers, M. Feraille, J. Lecomte, R. Divies and F. CokelaerSummaryIn the last decade, Deep Learning applications to unstructured data, such as images and texts, has known significant technical progress and democratization. However successfully adapting these technologies to geological data and activities is far from straightforward. As a contribution to the digital transformation of the subsurface industries, in this study we present three promising Deep Learning applications to unstructured geological data. The first use case is an automated classification of macroscopic rock samples pictures with convolutional neural networks. The second use case is an accelerated delineation of foraminifera micro-fossils on thin sections scans using segmentation algorithms. The third use case is an assisted mining of scientific texts to characterize hydrocarbon source rock formations, based on an entity extraction engine. From these use cases, we highlight the main challenges to expect in similar projects and share some good practices. Notably, we describe innovative methods to embed prior geological knowledge in the algorithms, to handle situations where only little training data is available, and to distribute the corresponding codes to geologists in user-friendly ways.
-
-
-
Analysis of Seismic Attributes to Assist in the Classification of Salt by Multi-channel Convolutional Neural Networks
Authors F. Jiang, P. Norlund and Z. WeiSummaryRecently, many deep-learning approaches have been applied to geophysical problems, such as seismic processing and interpretation, to aid in the exploration of hydrocarbon reservoirs. Convolutional neural networks (CNNs) are a popular new method to identify salt bodies in seismic data, by analyzing image segmentation and feature extraction. In this study, four ensemble classifiers were trained to analyze the importance of various seismic attributes with respect to the predictability of a salt body. By choosing seismic attributes with the highest importance as input data to a multi-channel CNN architecture, we successfully improved the accuracy of salt prediction. Both binary and multi-label salt classifications are shown, as well as comparisons of salt classification probability maps generated from models trained by seismic-only data vs models trained using seismic-plus-attributes data. The results demonstrated that using seismic-plus-attributes models significantly improved the continuity of salt boundaries and reduced unwanted artifacts, whilst also converging faster during training.
-
-
-
Preliminary Assessment of Structural Controls in the Sokoto Basin, Northwestern Nigeria Using Non-Evasive Techniques
More LessSummaryPreliminary assessment of the structural controls of the Nigerian sector of the Iullemmeden Basin, northwestern Nigeria has been carried out using non-evasive techniques. The Sokoto Basin deepens towards Niger Republic. Depth to basement interpretations from aeromagnetic data show eight major depressions in the basin comprising the Yerimawa-SabonBirni-Isah trough, Wurno-Rabah trench, Sokoto-Bodinga-Tambulwa trench, Tureta-Bakura ditches, Lema-Tambo sinks, Koko-Giro sinks, Gada holes and Kiwon Allah-Sokwoi-Illela pits. Structural interpretations show that three major fault lines trending NW-SE modified the sagged basement over geologic time. Integrating depth to basement and structural interpretations show that the Sokoto-Bodinga-Tambulwa trench, Kiwon Allah-Sokwoi-Illela pits and Lema-Tambo sinks are possibly connected by parallel faults trending NW-SE. Evidence from field studies of surface tectonic structures as well as the presence of a deep seated fault below the Wurno hill leads us to the conclusion that the Wurno hill is possibly tectonically controlled. Furthermore, the presence of a reverse fault and rollover anticline along Goronyo-Taloka road indicate possible convergent plate boundary and regional active faulting respectively. This may play a significant role in the maturity of organic rich sediments of the Taloka and Dukamaje Formations, flow of fluids as well as mineralization in the basin.
-
-
-
Study on Geological Feature Extraction from FMI Logging Data by Using Deep Learning Neural Network
More LessSummaryThis paper firstly studies the structure and algorithm principle of deep neural network which is divided into two processes of “pre training” and “fine tuning”, and it can avoid falling into local minimum and improve the learning speed. As an efficient feature extraction method, deep learning can complete the most essential description of the data.
-