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Third EAGE Digitalization Conference and Exhibition
- Conference date: March 20 - 22, 2023
- Location: London, United Kingdom
- Published: 20 March 2023
1 - 20 of 58 results
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Transformers for Site Assessment for Carbon Capture and Sequestration using Legacy Well Data
More LessSummaryCarbon Capture and Sequestration (CCS) is one of the vital components in keeping the global temperature rise within the 1.5 degrees Celsius target. Depleted oil and gas reservoirs are suitable locations for sequestering CO2 owing to their rock and structural properties and easy access to required infrastructure. Abandoned wells in these reservoirs can be used to inject CO2 saving both time and cost. Understanding the well integrity is important for CO2 containment and leakage prevention. It requires significant effort from a subject matter expert(SME) to identify well integrity information in legacy well documents which often results in longer lead times of up to an year for a CO2 sequestration site to mature. Transformer based pre-trained state-of-the-art models are utilized to inject useful information extracted from the well documents, which can be used instantly by SMEs to classify a well as potential high or low risk to inject CO2. Fine-tuning these models on generic datasets and less than 50 domain-specific examples shows that we can infer relevant well information not provided by traditional rule-based approaches. Reducing the lead time in maturing a site for CO2 injection could contribute to faster CCS project delivery timelines and broader goal of achieving net-zero targets.
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OSDU Journey in Totalenergies: from Concept to Operational Deployment
Authors T. Akimova, B. Joudet, E. Katimbo, M. Nsamba, P. Mungujakisa and D. LingoSummaryAfter the success of very ambitious OSDU SURINAME use case project delivered by TotalEnergies in 2021 it was decided to extend the pilot on an operational affiliate.
The Uganda affiliate was chosen as a pilot for the first OSDU deployment in operations with the objective to be delivered in Q2 2023. For the Uganda affiliate OSDU deployment is a great opportunity to do the “right thing” from the beginning by avoid data duplications, leverage existing applications and, identify fit-for-purpose digital solutions within the TotalEnergies portfolio to help affiliate face its operational challenges.
Uganda affiliate has a very ambitious drilling program with more than 400 wells in 4 years. The OSDU Data Platform will focus on 3 main phases from the well data workflow: well preparation, well operations and post-drilling phase.
The main applications supporting the workflow are connected to the platform by dedicated connectors. A big work stream of Data Management on the top of OSDU Data Platform is delivered to understand the data management systems maturity in the OSDU.
With the first operational deployment, TotalEnergies is convinced that it is a right time to demonstrate all the advantages of OSDU across the Company.
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A Geophysics Library of Trained Machine Learning Models
Authors A. Huck, P. De Groot and H. RefayeeSummaryMachine Learning models, which were trained to solve generic problems can be reused on unseen datasets. Applying such models is easy and valuable results can often be obtained much faster than through alternative workflows involving reprocessing and expert knowledge. Trained models therefore have potential to save time and money in operational settings by changing the way we work. Here, we discuss which problems are suitable, which type of models are available and how models can be added to a library of shared models. We will show examples of seismic and well log models that are applied to blind test data sets. These models are released in a library in the cloud that is accessible to users of OpendTect Machine Learning software.
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AWS Solutions for New Energy Wind Solar Farm Management
By N. MaSummaryAmazon is committed to building a sustainable business for our customers and the planet. In September 2019, Amazon co-funded The Climate Pledge — a commitment to be net zero carbon across our business by 2040, 10 years ahead of the Paris Agreement. As part of this pledge, Amazon has made ambitious commitments toward reaching this goal. Amazon is the world’s largest corporate purchaser of renewable energy and is on a path to powering operations with 100% renewable energy by 2025.
Amazon Web Services (AWS) is known as a cloud innovator. The AWS energy transition wind solar farm data management and operational dashboard allows for the efficient and effective management of data and operations for wind and solar farms by adopting AWS cloud native services. The dashboard allows users to monitor and track energy production, manage maintenance and repairs, and optimize performance through data analysis and visualization.
AWS is sharing solutions developed internally and is enabling our customers and partners to build solutions on top of what we believe are the broadest and deepest set of cloud services available to accelerate customer’s energy transition.
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An Integrated Workflow for Windfarm Site Characterization by Deep Learning
Authors H. Di and A. AbubakarSummaryWith the target of net-zero carbon emissions by 2050, the demand for renewable energy is increasing exponentially, and offshore wind farms are identified of the most potential of investment. Developing an offshore wind farm requires robust characterization of the near subsurface soils for turbine foundation design, construction, and monitoring, which faces with many challenges related to seafloor topography mapping, shallow geohazard detection, structure interpretation and modeling, soil type analysis, geotechnical parameter estimation and so on. This work revisits these existing challenges in from the perspective of pattern recognition, investigates the feasibility of deep learning in resolving them, and proposes an integrated workflow of great potential in accelerating the process of windfarm site characterization. Its values are demonstrated through applications to the public HKZ dataset within the Dutch wind farm zone for accelerating three major tasks, including picking multiple major horizons, mapping the seafloor topography, and estimating the essential geotechnical parameters. Future efforts are expected for more components in the proposed workflow for further integration and automation.
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AUTO Channel: AI-driven Automatic Channel Scalar Correction
Authors C. Sutton, Y. Sang, W. Pan, P. Webster, J. Chen and M. SidahmedSummarySeismic shot processing is critical for the quality of subsurface images, and incorrect shot processing parameterization can degrade an image. Within the realm of deepwater streamer acquisition, channel amplitude correction compensates for amplitude variation among different receivers. However, it can take days for multiple parameterization efforts to perform the conventional processing, which involves manually iterative optimization of processing parameters based on the quality control (QC) of processing outputs. Artificial Intelligence (AI) is proven to be able to perform human-level QC with reduced processing cycle time. Therefore, we propose a new workflow combining a new AI-based QC agent with an automatic optimization method to automate channel amplitude correction, thus reducing the cycle time. The automatic workflow has been successfully applied to multiple Shell assets.
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Data-Centric, Interactive Deep Learning for Complex Geological Features: a Groningen Case Study
Authors S. Salamoff, J. Chenin, B. Lartigue and P. EndresenSummaryDetailed interpretation of complex facies intervals within high-resolution 3D seismic data is a tedious and time-consuming process, even with the assistance of traditional deep learning methods. Traditional, windowed waveform classification algorithms can have a non-unique solution and are impacted heavily by interpreter bias and laterally varying data quality. This is the case in deformed facies intervals, such as post-depositional deformation of complex geological sequences, where tectonic reactivation and/or salt tectonism have re-worked sequences of post-salt siliciclastics into complicated packages that are difficult to interpret. These heavily reworked zones are prolific throughout the North Sea and can play an important role in fluid migration and containment. With manual interpretation methods, it is extraordinarily difficult to map these re-worked sediments. Their complexity usually means such sequences are under-interpreted, which introduces pre-drill uncertainties about the well path or target itself. Therefore, we propose a new, data-centric, and interactive deep learning methodology that leverages neural networks to accurately predict separate deformed facies in the Groningen Area. The results were obtained in a fraction of the time compared to traditional interpretation workflows and allow geoscientists to better characterize complex geologic units while also determining its impacts on prospective petroleum systems or planned well paths.
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Using Machine Learning Property Modeling with Assisted Forward Stratigraphic Modeling for Offshore Wind Farm Site Characterization
Authors A. Ahmad, K. Eder and S. CourtadeSummaryThe integrated workflow discussed in this abstract is a combination of two-2 step approaches for creating predictive models for understanding the shallow sediment distribution effectively and to reduce the related uncertainty when it comes to designing and feasibility of piling foundations for Offshore Wind Farms. It is important to mention here that this workflow is essential and driven by the generation of answer products for reducing uncertainty on the foundation installation of Offshore Wind Farms, by creating predictive sediment models and use of these in estimating capacity for foundational installations by deploying machine learning property modeling workflow.
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Give Geological Context to Seismic Attributes Through Artificial Intelligence, Using Neural Style Transfer
By R. PerezSummaryIn the oil and gas industry, seismic attributes are used to study and understand the subsurface geology. However, their usefulness is limited by a lack of sufficient geological context. In this work, seismic attributes (spectral decomposition) are put into a geological context using the neural style transfer (NST) algorithm to visualize a paleoriver system.
To transfer the style from the reference image to the content image, the stylized image is initialized with the content image, and the total loss is optimized with respect to the pixels of the stylized image. Adam optimizer is used and the content weight and style weight can be adjusted to control the relative importance of the content and style in the final stylized image.
The output image demonstrates how the stratigraphic feature highlighted by the spectral decomposition attribute would appear if it were captured from a satellite image today. This output image is easy to understand for anyone, with none to low expertise in geoscience. Neural style transfer can be a valuable tool for analyzing and visualizing stratigraphic systems.
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Manual Active Learning for Salt Interpretation: an Empirical Study to Avoid Forgetting During Incremental Trainings
Authors L. Evano and F. CubizolleSummaryIn seismic applications, the labelling is a challenging and tedious task due to the broad areas covered by the seismic data and requires expert knowledge. Consequently, finding solutions to limit the labelling effort is a priority to accelerate workflows and to optimize the human resources. The technique of active learning can help in reaching these goals. It consists in selecting the best data to label in order to improve the model performance based on an iterative approach during which, at each step, unlabeled data are chosen to be labelled and used to train the model. This process is repeated until the model reaches acceptable performances. The main challenge when incrementally training a neural network is the forgetting of the patterns learned during the previous training iterations. We showed that the choice of the old/new labels ratio in the training and validations sets, as well as the choice of the learning rate and the patience can help mitigate the knowledge loss in the case of incremental trainings.
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Futureproofing Rich Metadata File Ingestion with OSDU
Authors T. Hewitt and R. GadrbouhSummaryActing as a technology-agnostic, standards-based data platform, the OSDU has reduced energy data silos and provided the capability for applications developers to build new solutions and data ingestion services.
The current OSDU schemas are primarily created to store file metadata to allow users to query common business content that can be extracted from the files. We utilized a machine-learning and subject matter expert classification process to auto-generate detailed file metadata for millions of files and ingest them directly to the user OSDU instance with source files.
The file classification process currently generates a graph database representation of files and rich metadata labels at a data-object level. The classification results, alongside data lineage and quality, are stored in OSDU work product components and datasets and ready to migrate to the OSDU platform.
The process prevents users having to manually fill or supply the file manifests during file ingestion to their OSDU implementation. With over 700 distinct data types and 250,000 entities of subsurface terminologies, millions of ingested files can be enriched with highly granular metadata manifests that guarantee rapid data search and access to high-quality data.
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Automated Extraction of Images of Interest in Document Collections: End-to-End Workflow and Operational Case-Study
Authors M.T. Nguyen, C. Cornet, L. Mattioni, A. Bouziat and G. RumbachSummaryData extraction is the process of analyzing and transforming unstructured information into structured data. Structured data can then generate meaningful insights for reporting and analytics in companies. Automation of such tasks can improve the efficiency of operational workflows and help professionals save time for more advanced and higher-value activities in their daily work. Recently, Machine Learning, Computer Vision and Natural Language Processing have been intensively developed and largely employed to automate information extraction. However, still few practical case-studies on operational geoscience data are documented. In this paper, we develop an integrated workflow to automate the extraction of images of interest and the associated information in geoscience documents. The developed workflow relies on a combination of free Python packages for Natural Language Processing, Computer Vision, Optical Character Recognition and Machine Learning. This workflow was applied on a case study using data from the LUGOS Oil Field. The objective was to automatically extract and document the evolutive interpretation of principal structural maps during several decades of field development. The proposed workflow provided very positive results, as the whole automated process had a success rate above 90% on the case-study, while lasting only 5 hours instead of several weeks of manual work.
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Seismic Tiles, a Data Format to Enable Analytics on Seismic by Digitalization of G&G Logic
Authors Ø.M. Skjæveland, S. Torset and C.C. NilsenSummarySeismic Tiles is a data structure for representing seismic reflectors in tabular form. A tile is a small surface, (reflector segment) that aligns with a seismic reflector. In a similar fashion as how the roads of Google Map are represented by connected road segments, connected tiles will represent seismic reflector surfaces. The value of Seismic Tiles is similar to the value of the Google Map data structure, in that logic now can be applied to seismic reflectors in a straight-forward way. We can now automate interpretation tasks such tasks as prospect identification, 4D anomaly hunting, faults and horizon interpretation) by explicit (and thus explainable) logic. In contrast to the machine learning (ML) way where the machine learns by example, Seismic Tiles allows interpretation logic in “digitalized” form to be applied directly. In contrast to ML approaches, this process does not require any training data, and is fully transparent in its workings. We believe this can be a game changer in the automation of seismic analysis, as Google Maps style technology has been in road navigation.
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Well Control Optimization Using Smart Proxy Models
Authors A. Jahanbani Ghahfarokhi and A. ChaturvediSummaryOptimal well controls to maximize the net present value (NPV) are usually obtained by coupling the numerical reservoir simulator with optimization algorithms. This approach requires significant number of simulations that are computationally expensive. Proxy models have a high capability to identify complex dynamic reservoir behavior in short time.
This study proposes a methodology by developing smart proxy models (SPMs) using Artificial Neural Network (ANN) for a synthetic field model to predict field production profiles. The method then integrates the established proxy models with Genetic Algorithm (GA) to solve the well control optimization problem. From SPM-GA coupling, the optimum well control parameters, namely bottom hole pressures of the injectors and producers are investigated to maximize NPV.
The developed SPMs produce outputs within seconds, while the numerical simulator takes an average time of 30 minutes for the case study. SPM-GA coupling works well for well control optimization by finding BHP configuration that gives an increase of over 30% in NPV, and requires fewer simulations compared to the traditional approach. The results show that the established proxy models are robust and efficient tools for mimicking the numerical simulator performance in well control optimization. Significant reduction in computational time and resources is observed.
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A Simple Machine-Learning Approach for the Discovery of Digital Subsurface Geoscience Analog Data
Authors S. Sheyh Husein, R. Vhanamane, A. Laake, F. Stabell and M. D SouzaSummaryThe need for accelerating and improving the quality of opportunities in the asset maturation life cycle encouraged us to develop a digital solution to help geoscientists extract hidden value in their structured datasets. The focus was on creating an unsupervised machine-learning (ML) algorithm that can be trained on a structured dataset to enable the geoscientist to be presented systematically with a ranked list of analogs that meet a predefined set of weighted criteria. This has time-saving and quality-improving implications for prospect risk and volume screening, benchmarking, quality assurance and subsurface insights. The ML-assisted analytics workflow will result in more confident estimates of volumes and risk, and a list of similar reservoirs that can provide insights and new interpretation scenarios.
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Reuse and Recycle Knowledge, Not Only Data
Authors C. Warren, N. Masurek, A. Laake and C. WraySummaryExploration, development, and investment decisions in any energy project requires consistent, trusted, and auditable technical and economic support material, that often takes too long to create and delivers results that are not comparable. In particular, valuable time is wasted while trying to find adequate knowledge and data or by duplicating work. If the users could easily access, consume, and recycle corporate knowledge digitally, which contains the direct link back to the raw data, considerable time and cost would be saved and results would be more consistent helping to support corporate decisions.
One technology to efficiently search corporate digital knowledge is the knowledge graph using defined ontologies enabling deep and efficient utilization of corporate knowledge. The knowledge graph accommodates the relationships inherent to the knowledge and associated data. By using a graph database, information can be accessed that enables users to find the knowledge and data they require, thus saving valuable time and cost.
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The Required Value of Open Digital Platforms: an Example of Connection to Third Party Applications.
More LessSummaryNew digital journeys should easily integrate with already well-proven technology adding value to open digital platforms that account for some Application Programming Interfaces (APIs) that allow users to connect to third party applications. The digital footprint of all this data becomes essential, with new ways of analysing the results and new workflows that can utilise other cloud and non-cloud based existing solutions to create new insights and value to decision makers. Interconnecting applications by exporting results, exchanging tokens and validating users as well as ingesting results from other applications seamlessly becomes key to maximise technology investments. In that way we can expand the capabilities of new workflows, without having to focus on developing technology that is already existing, nor having to duplicate data. The results of the running external engines, once dynamic simulation has been completed, get stored in the original contextualised cloud service for further analysis and results evaluation. Extracting value from digital data should therefore not be about a scattered search for some relationships in data, but having a deliberate approach to query the data for the information the energy industry could utilise for specific decision making.
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Visualize OSDU™ Data with Geospatial Consumption Zone and No Code Maps
Authors B. Boulmay, Y. Gubanov and D. TishechkinSummaryGeospatial data integration remains a key challenge across the Energy Industry. Join AWS and Esri as they explore the latest subsystem developed for The OSDU Data Platform. A large cross industry working group of Operators, Independent Software Vendors (ISVs) and Cloud providers came together to build the Geospatial Consumption Zone (GCZ) to enable easy access to map-based Application Program Interfaces (APIs) representing all of your data in OSDU. The presentation will touch on OSDU, how the GCZ capability was started, some of the technical architecture and finally demo how you can use the map services today in a no code environment to enable access to OSDU content for search, analytics and visualization. This platform approach and openness of OSDU is helping to accelerate digitalization across the industry.
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Rock Property Prediction Ahead of the Drilling Bit Using Dynamic Time Warping and Machine Learning Regression
Authors A. Christ, A. Bouziat, C. Cornet, Y. Djemame, J. Fortun, J. Lecomte, A. Meunier and P.N.J. RasolofosaonSummaryIdentifying the lithology while drilling is a crucial part during geosteering when drilling a new well. Conventional geosteering uses extensive seismic, geological models, borehole images which are not necessarily available in an exploration context. In such challenging context, where only scarce data are available (e.g., Gamma ray (GR) log), we propose a new method for predicting logging responses ahead of the drill bit upstream of geosteering workflow. The method is based on performing machine learning regression and dynamic time warping on available well log data from neighboring wells as well as from the currently drilled well. Combining both technologies allows to reliably predict formation rock properties ahead of the drill bit and therefore enables to guide the geosteering in anticipation of future lithology changes. The prediction can be done in near real-time while drilling because the computational time of only a few minutes is largely inferior to the drilling time for such a distance, which is typically longer than 6h. We successfully applied this method to well log data from Offshore Western Australia and could predict the GR response up to 100m ahead of the drill bit. The proposed workflow is easily transposable to any other well log data.
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Predicted Stratigraphy: A Case Study from the Sureste Basin (Gulf of Mexico)
Authors P. Kiss, J. Clayton, J. Cullum, W. Lee and U. AkramSummaryBiostratigraphy represents one of the key disciplines of geology by allowing the arrangement of geological formations in space and time based on fossil assemblages. Due to its significance in the oil and gas industry and the fast pace of technological innovations and developments in geosciences, the interpreted biostratigraphical data is prone to become quickly outdated and thus preventing its use in future interpretations. However, the presence of a large amount of available data provides an excellent opportunity for novel studies aiming to update species taxonomies, detect reworking specimens, train machine learning models and test prediction models in order to digitalize biostratigraphic approaches. In this study therefore, we use various data science and machine learning techniques to demonstrate the potentials of an automated biostratigraphic approach. We take advantage of legacy data collected in the Sureste Basin, Gulf of Mexico, where we transformed the original dataset into a final stratigraphic framework. Our inferences indicate that we can get an accurate first insight into the stratigraphy at the studied location within a very short timeframe. Even though inconsistencies were found, our approach proved its potential for future work, which could be improved by increasing the prediction accuracy of biostratigraphic events.
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