- Home
- Conferences
- Conference Proceedings
- Conferences
Third EAGE Digitalization Conference and Exhibition
- Conference date: March 20 - 22, 2023
- Location: London, United Kingdom
- Published: 20 March 2023
41 - 58 of 58 results
-
-
A machine learning methodology to forecast well production: illustration with the Volve dataset
Authors B. Auffray, T. Duval, G. Suzanne and M. FerailleSummaryThis abstract presents the workflow built in order to forecast well production parameters and its application on the Volve field dataset. It consists of a sequence of four neural networks trained to forecast well downhole pressure, total liquid rate, water-cut and wellhead pressure. This workflow allows the engineers to rapidly evaluate scenarios for their day-to-day optimization operations.
-
-
-
Exploring Volume Data Store format to speedup seismic image segmentation in the Cloud
Authors L. Boillot and G. FussSummaryImage segmentation is key in seismic interpretation, aiming at detecting geological object at pixel scale in 3D cubes. Nowadays techniques based on deep learning accelerate this tedious task. In practice, the cubes are split into 2D images following different directions and the results are then restituted into cubes. These directions are the orthogonal axes but also surfaces that are not parallel to any axis. Legacy data storage formats stack the 3D cube into 1D array, offering straightforward memory access only for aligned or strided patterns. Typically, the seismic slices or horizons are the worst cases and lead to important access time. Volume Data Store (VDS) is a storage format that has been designed by Bluware Inc. to tackle this issue, with a contribution to the Open Group OSDU Data Platform. In this paper we proposed a benchmark of this VDS format against a legacy format such as SEG-Y and SEP. Different parameters are taking into account especially the computing capability of the machine specification. The preliminary results show an important speedup of VDS format in the slice direction of extraction. Further comparisons are on-going, involving VDS special capabilities like use of compression and decimation.
-
-
-
Improving Reservoir Property Prediction Using Synthetic Data Catalog and Deep Neural Network in Poseidon field, Australia
By P. DidenkoSummaryThe ultimate goal of reservoir characterization is to predict the distribution of elastic properties, porosity, and fluids in the target area. For many years Machine Learning techniques have been used in geophysics for different applications, including reservoir property prediction.
In these supervised learning approaches, the relationship for predictions is derived from the data. One of the major limiting factors for these workflows is the lack of labelled data covering the expected geology, therefore, it is challenging to train the neural network sufficiently. To overcome this, the hybrid Theory-Guided Data Science-Based method was applied.
The aforementioned workflow is divided into two main steps: first, generate many pseudo wells based on the statistics of the real well data in the project area. The reservoir properties, such as porosity, thickness, water saturation and mineralogy, are varied to cover different geological situations. Elastic properties and synthetic seismic gathers are then generated using rock physics and seismic theory.
The resulting set of synthetic data is used to train the neural network. The operator, derived during neural network training, is then applied to the real seismic data to predict properties throughout the seismic volume.
-
-
-
Digital Levers to Cut Cost and Risk of Gas Supply and Carbon Capture Storage in Transition
Authors K. Armitage, A. Hardwick, T. Brierley, P. Mewett and G. RobertsSummaryWe present a workflow (Rejuvenate) that reduces cost by using big data, rule based and expert systems (ES) integrating geology and geophysics datasets for the energy transition such as Carbon Capture and Storage (CCS). The implications are a substantial reduction in risk, cost, and confidence in reservoir properties.
ES derives geology from seismic data itself. The workflow provides resources and intelligence to clients so that green gas with CCS can bridge the gap to sustainable renewable energy towards a net-zero target. By background, a major oil company drilled 17 exploration wells spread over several sub basins. All wells were dry. Using our approach, we found that the information existed in the seismic and ancillary data that could have avoided this expense. ES is based on decades of research into dry wells and associated seismic, well data and geology with patents in place. Proven to increase efficiency by more than fifty percent, anomalies can be identified in geology and directly linked to seismic patterns. This learning can now be migrated to Machine Learning (ML) using risk matrices for the wells of today and the future; in essence a knowledgebase of seismic that did not fit the real geology that adapts.
-
-
-
MPI-free FWI for Cloud Spot Markets: Faster and Cheaper Results
Authors C. Mavropoulos and A. UmplebySummaryAs the industry shifts to more computationally intensive data-driven applications, so does the need for more scalable and efficient processing power. Running such applications on the cloud is the obvious solution as the resources can scale per the requirements and stage of the project. We propose an Infrastructure as Code (IaC) environment: S-Cube Cloud (SCC) to launch and control large volumes of computational resources needed for new seismic processing applications. To effectively leverage the cloud, spot instances must be utilised, which are offered at a large discount but may be interrupted at any time. A key limitation we address is the absence of an efficient and fault-tolerant parallelisation scheme which is cloud-native as, without it, usage of discounted spot instances is unachievable. We propose RIPS(SCI) - Robust Inter Process Simple Socket Communication Interface - which allows for the utilisation of spot instances through its fault tolerance. Applied in real-world conditions, RIPS communicates between thousands of instances and handles spot instance interruptions. Furthermore, RIPS relieves major bottlenecks in the master process bypassing processing terabytes of data per iteration compared to MPI. Savings of 70%–80% are observed in processing workloads in customer workflows using spot instances enabled by RIPS.
-
-
-
Citizen Data Scientist Toolbox for petrophysicist domain experts: case study Petroleum Industry of Serbia
Authors T. Micić Ponjiger, S. Šešum, M. Naugolnov, S. Perunić and V. MihajlovićSummaryMain scope of this paper is to present a tool created for petropysicists in Petroleum Industy of Serbia, in order to perform advanced analytics and machine learning (ML) models as a citizen data scientist. A petrophysicist as a citizen data scientist creates or generates the ML models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but with primary job function outside the field of statistics, analytics and computer science. By using the standard standard software platform for petrophysicist to implement Citizen Data Scientist Toolbox we minimized the negative acceptance outcome, typical for new digital tools and applications in industrial companies.
-
-
-
Predicting Geologic CO2 Storage and Plume Evolution from Sparsely Available Well Data Using Barlow Twins
Authors C.A.S. Ferreira, T. Kadeethum and H.M. NickSummaryCarbon Capture and Storage (CCS) is an important practice for reducing greenhouse gas emissions and combating climate change. However, accurately monitoring carbon storage operations using simulations can be challenging due to data availability, subsurface complexity, uncertainty, and computational cost. Machine learning can help to address these challenges by providing cheaper data-driven approaches. For instance, Continuous Conditional Generative Adversarial Networks (CCGAN) can be used to predict CO2 plume propagation with sparsely available data. This model enables fast prediction with reasonable accuracy and a substantial reduction in computational cost when compared to numerical simulations. Another approach, Barlow Twins (BT), provides better results than other deep learning-based approaches and comparable results to traditional methods for linear subspace and nonlinear manifold problems. In this work, we compare the accuracy of predictions of CO2 plume propagation based on data from three well locations using a BT-based approach to those obtained with the CCGAN. Our findings suggest that BT-based approaches could be a viable option for data-driven simulation of CO2 plume propagation in the subsurface when data is limited.
-
-
-
Productization of Digital Transformation in the Subsurface
By C. HantonSummaryDigital Transformation has become familiar lexicon to those in the subsurface departments of energy operators in the past decade. But while the term is industry wide, the effects and benefits do not have such distribution. Success stories in the media are dominated by large NOCs and super-majors, while surveys show that the industry as a whole, lags behind its peers in effectiveness and success of digital transformation effects.
In 2022, our industry was given clear goals - provide cheap, reliable and low carbon energy to the globe; solving the ‘Energy Trilemma’ as it became known. To achieve this, low-cost, highly efficient operations are a fundamental requirement, ensuring rapid development of existing assets and assisting in both near-field and exploration appraisal. Geoscientists are key to enabling this by developing insightful views of the subsurface that inform the decision making process, but face additional challenges of restricted resources due to high levels of turnover during the Covid-19 pandemic.
In this paper, the author explores common flaws in implementation of digital technology and explores how productization of digital transformation initiatives could yield increased success rates while reducing delivery times for operators of all sizes.
-
-
-
Machine Learning Application for Joint Rock-Physics Model Optimization, Facies Classification and Compaction Modeling: North Sea Example
Authors R. Filograsso, A. Mur, R. Beloborodov and M. PervukhinaSummaryPresentation of results of rock physics guided machine learning method to improve efficiency of geoscience workflows by automating petrophysical facies log interpretation, petro-elastic depth trend production and rock physics model parameterization. We present a case study for an inversion of 7 wells set in Central north Sea, within the Forties field. The application of the new rock-physics guided machine learning toolkit demonstrates the versatility of the application, agreement with manual facies interpretation, and importance of cross-disciplinary integration.
-
-
-
Exploring the Potential of Denoising Diffusion Probabilistic Models for Generating Realistic Geological Rock Thin Section Images
By R. PerezSummaryThis study examines the use of Denoising Diffusion Probabilistic Models (DDPMs) for generating realistic geological rock thin section images. The accuracy and realism of DDPM-generated images are evaluated and compared to real-world photographs. The results indicate that DDPMs can produce high-quality samples that closely resemble real-world samples and could potentially offer a solution to the challenges associated with obtaining and maintaining geological rock thin section photographs. Some suggestions for future research include looking into how DDPMs could be used in other areas of geoscience, coming up with ways to get around their limitations, and using other machine learning techniques to improve the accuracy of the images they create.
-
-
-
Flowify: Simple and Non-Technical User Interface for Workflows Managements with Reusable Components
Authors O. Nilsson, L.P. Turchan and A.T.K. ChengSummaryRapid uptake of programming literacy in the industry has created a growing class of low-code developers while no-code users continue to dominate our workforce. To bring products from low-code developers to the no-code general public requires competing for limited professional developer resources. In response to these needs, Equinor designed and built Flowify, an open-source software that enables the full potential of users without experience in the field of programming.
Flowify provides a fully graphical interface for building, managing, and executing workflows. Through this domain experts can run complex workflows in a scalable manner without the need for programming expertise. The system features the ability to create workflows from existing components, create new components, and scalable execution. The Flowify system is fully cloud native and interoperates seamlessly with modern cloud solutions, such as various data sources, credential stores and others.
-
-
-
Natural Language Processing for Key Geology Information Detection and Geological Description classification
Authors H. Blondelle and O. Nguyen-ThuyetSummaryBERT and GPT3 make possible the Natural Language Processing of Geosciences reports at an other level. Today, legacy documents can be turned in a source of actionnable data to built chabots, classify documents automatically according their content or to search for the key data, information and knowledge within the documents. This paper will discuss about the advantages of the transformers algorithms to capture the meaning of the technical reports and the possibility to tune the pre-trained models to adapt them to a geoscience context. This experience will be illustrated using a recent case study developped for the mining industry.
-
-
-
3DMadNex AI: New Digital Tool for Operational Geology Virtual Assistance and its Deployment in Middle East
Authors P. Coraci, F. Di Maggio, R. Lopez, F. Porcelli, R. Sabatino, F. Marchini, A. Di Palo, S. Pianaro, A. Crottini, M. Pirrone, F. Chinellato, M. Pelorosso, P. Tempone and C. SanasiSummaryThe energy industry is facing unprecedented challenges, whose solution requires an integrated effort and higher collaboration among the different disciplines. In such operating context, the availability of quality data and the ability to have an efficient interpretation process has become key for subsurface. Over the last few years, Eni has developed innovative geological interpretation tools, leveraging the internal know how and the most up-to-date technology in the market. The range of applications has been progressively expanding and the tools are today adopted in several geographical units and geological contexts. This paper will present one of the latest achievements, the introduction of the 3DMadNex AI suite during the drilling of four wells in different formations (clastic and carbonate reservoirs) in the Middle East.
-
-
-
A Deep Learning Seismic Processing Framework Based on Pre-Training: Giving the Dataset the Attention It Needs
Authors T. Alkhalifah and R. HarsukoSummaryEvery seismic dataset has its particular characteristics guided mainly by the property of the subsurface it covers, the data acquisition parameters (the survey), and by the often unique noise condition for every dataset. Capturing such characteristics in a neural network model for efficient application of processing tasks offers a more effective approach to incorporating machine learning than training neural networks for specific tasks that may or may not transfer well to new data. We use a framework for seismic processing that allows us to pretrain a neural network to learn the features of a seismic dataset, and then fine tune the network for any downstream processing task. We take advantage of the fact that most processing tasks utilize the same features embedded in the seismic dataset, and thus, these features can be stored in a common pre-trained network in a self-supervised manner, we refer to as StorSeismic. We provide insights into the framework as it captures the seismic dataset features. Then, we use the labeled synthetic data to fine tune the pre-trained network in a supervised fashion to perform various seismic processing tasks, like denoising, low frequency extrapolation, first arrival picking, and velocity estimation, with satisfactory results.
-
-
-
Well Data Quality Assurance: How to Ingest, Validate, Publish, and Manage Data for Real Time Applications
Authors P. Tempone, C. Piras, N. Lamonaca, M. Biagi, L. Biagiola, C. Occhiena, A. Crottini and C. SanasiSummaryWell Operations involve data acquisition by several service contractors, who operate with a variety of formats and internal practices. Operating Companies have, on the other hand, the very practical need to collect, access, audit and make such data available on a centralized source to be distributed according to internal governance and business processes. We describe how this is implemented in the Eni’s central hub for real-time well data management.
-
-
-
Creating a National Data Asset: Detailing the Digitalization of the UK’s National Data Repository
Authors J. Nicholson, C. Jones, J. Selvage and A. ThompsonSummaryThis paper details the application of digital cloud technology to the UK’s National Data Repository (NDR), shares applicable learnings and highlights areas of workflow evolution that have delivered value.
We seek to decode the digital building blocks used to construct the National Data Repository provision and illustrate how data is quality assured at source.
Examples are provided, illustrating the dramatic improvement in data quality and compliance with defined standards since the adoption of the new digital system, and crucially, how the methodology has protected the newly created data lake enabling preservation of quality data as a national asset.
These datasets, originally acquired for petroleum exploration and production, are being used in emerging use cases including de-risking offshore wind projects, strategic gas storage assessments, nuclear waste management in addition to continuing UKCS hydrocarbon exploration.
We will discuss how a data collection of just 15TB after 15 years of service has been transformed to now hold ∼ 400TB after eighteen months and is projected to grow to several petabytes as modern and historic data is reported to the public cloud NDR and published for re-use
-
-
-
A Citation Network Analysis on Diffusion of Technologies to Other Fields: a Case Study About FWI
Authors S. Masaya and Y. NishitsujiSummaryIn recent years, the rapid changes in social trends and technologies, such as digital transformation and energy transition, have had a large impact on many industries. Future forecasts and exploration of potential values become indispensable for dealing with such changes and achieving the success of novel research and/or business. In this paper, we discuss an approach to evaluate the diffusion of innovative technologies to other fields using the network data in academic articles citing a review paper. This study provides a case study of full waveform inversion as an example in exploration geophysics to demonstrate the effectiveness of the approach by using the Web of Science database. This analysis enables us to forecast the trend of technologies by analyzing the diffusion of the other technologies as well as full waveform inversion.
-
-
-
Orchestrating Coopetition in the Development of OSDU Data Platform
By M. MoradiSummaryCollaboration and crossing organizational boundaries is a key to tackle challenges such as pursuing digitally enabled innovation initiatives in the oil and gas industry. Lack of effective collaboration and orchestration mechanisms might pose challenges on effective sharing, accumulation, and creation of knowledge, and the innovation outcome. Therefore, it is important to learn more about efficient orchestration mechanisms to manage a coopetition and to create a connectivity-based business model. The development of Open Subsurface Data Universe (OSDU) is a great example of inter-organizational and industrial collaboration. Study of collaboration among different stakeholders in development of OSDU data platform helps the members of the industry to discover the best way to facilitate knowledge sharing and value co-creation and build industrial competitive advantage. This research is based on a case study of the evolution of the OSDU data platform. By focusing on social side of platform co-creation the results show that a shared orchestration is a key to organize complex innovation projects.
-