Webinar #67: Rapid Forecasting of CO2 Plume Migration in Varied Geological and Engineering Scenarios utilizing Transfer Learning
Speakers: Dr. Siddharth Misra, Texas A&M University
In comparison, FNO alone took 12 seconds for one scenario, while the CMG simulator required 40 minutes. The proposed method thus provides an efficient and reliable means of predicting CO2 plume migration in large storage reservoirs under varying conditions.
Webinar #65: Addressing the Computational Challenges for Modeling CO2 Storage with GPU
Speakers: Dr. Vincent Natoli, Dr. Leonardo Patacchini
Webinar #62: Differentiable Programming: Bridging the Gap between Numerical Models and Machine Learning Models
Speaker: Dan O’Malley, Los Alamos National Laboratory
Webinar #59: Uncertainty Quantification for Transport in Porous media using Parameterized Physics Informed neural Networks
Speaker: Cedric Gasmi, Stanford University
Webinar #58: Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR: LLNL Task4 Prototype
Speaker: Dr. Hewei Tang, LLNL
Webinar #56: Virtual Digital Twin for Real-Time Integrated Power Plant Control Room and Field Operations Research, Training, and Education
Speaker: Dr. Stephen E. Zitney, Process Systems Engineering Research, National Energy Technology Laboratory (NETL), U.S. Department of Energy (DOE)
Webinar #51: Reservoir Analysis of a CO2 Sequestration Site: Experiment-Guided Field Scale Modeling
Speaker: Similoluwa Oduwole, Master’s Candidate in Petroleum Engineering at Colorado School of Mines
Geosequestration of CO2 requires guided transport to and reliable quantification of the storage capacity of geologic formations as well as safety assessment to ensure permanent retention of the injected fluid. We present a workflow based on the analysis of well logs, experimental insights, and reservoir simulation to better map CO2 saturation distribution from time-lapse seismic data. CO2 saturation maps are typically derived from 4D seismic data using rock physics models to assess fluid saturation and pressure effects. We present here a combination of our rock physics understanding to guide reservoir flow simulations. We find that in addition to fluid saturation changes, fluid reactions with the injected CO2 might better explain 4D seismic amplitude change maps.
Webinar #49: Developing CO2 storage in depleted gas fields
Speaker: Dr Filip Neele, CCS Applied Geoscientist, TNO
Depleted oil or gas fields offer interesting opportunities for storage of CO2. They have a proven seal and trap and if the timing is right production facilities could be re-used. In The Netherlands, three consortia are preparing plans for large-scale storage in offshore depleted gas fields. In this presentation, the challenges of developing safe injection scenarios are discussed. Due to the often low pressure after production, the CO2 has to be brought from the high pressure in the transport pipeline to the low pressure in the deep subsurface. This process needs to be managed, to avoid too low temperatures in the well or the reservoir. Additional challenges are encountered in monitoring such projects; examples are shown related to flow distribution over injection wells.
Webinar #47: National Risk Assessment Partnership Phase II Research: Tools and methods to quantify subsurface environmental risks at geologic carbon storage sites
Speaker: Dr. Robert Dilmore, National Energy Technology Laboratory
A key to enabling commercial-scale deployment of geologic carbon storage (GCS) is ensuring that GCS sites will safely and effectively sequester large volumes of CO2 out of the atmosphere for hundreds of years, or more. To address that need, the US DOE’s Office of Fossil Energy and Carbon Management sponsored the National Risk Assessment Partnership (NRAP) – a multi-year, multi-national laboratory collaborative research project focused on developing and demonstrating methods and tools for quantitative assessment and management of subsurface environmental risks associated with GCS. Through NRAP’s second Phase of research researchers have developed a computational toolset to support aspects of stakeholder decision making related to evaluating long-term containment effectiveness, assessing risk of unwanted CO2 and brine migration, quantifying and managing potential induced seismicity risks, and design effective and efficient monitoring networks to detect potential leakage. NRAP has also released a set of (draft) recommended practices for assessing and managing leakage and induced seismicity risks at GCS sites, and built a catalog of use cases that demonstrate those tools and methods. The NRAP research team promotes the testing, use, and refinement of these products by the international carbon capture, utilization, and storage research, development, and deployment community.
Webinar #40: CarbonSAFE Initiative
Speaker: Mary Sullivan, National Energy Technology Laboratory
The Carbon Storage Program implemented by the U.S. Department of Energy’s (DOE) Office of Fossil Energy and managed by the National Energy Technology Laboratory (NETL) is helping to develop technologies that safely and permanently store carbon dioxide (CO2) without adversely impacting natural resources or hindering economic growth.
Since its inception in 1997, the Carbon Storage Program is developing and advancing carbon capture and storage (CCS) technologies both onshore and offshore that will significantly improve the effectiveness of the technologies, reduce the cost of implementation, and be ready for widespread commercial deployment. The portfolio includes industry development projects, university research projects, national laboratory research (including research conducted at NETL), and international collaborations to leverage global expertise, test facilities, and field sites. The Program approaches these challenges through integration of two components: (1) individual technologies developed through R&D, and (2) field lab testing sites.
Building upon almost two decades of knowledge and experience gained from the field lab testing projects and Regional Carbon Sequestration Partnership (RCSP) Initiative efforts, the Program initiated the Carbon Storage Assurance Facility Enterprise (CarbonSAFE) Initiative. The CarbonSAFE addresses key gaps on the critical path toward CCS deployment by reducing technical risk, uncertainty, and cost of a geologic storage complex for 50+ million metric tons of CO2 over a 30 year timeframe from industrial sources. The CarbonSAFE Initiative is taking a phased approach: (1) the Integrated CCS Pre-Feasibility phase, (2) the Storage Complex Feasibility phase, and (3) Site Characterization and CO2 Capture Assessment. Subject to the availability of funding, a fourth phase would include permitting and construction. A total of 13 projects were completed under Phase I, which provided high-level evaluations of potential CCS scenarios in regions throughout the United States. Now nearing completion, six projects were awarded under Phase II to confirm the adequacy of storage complexes through initial site characterization. Phase III projects involve the entire integrated process; including a capture assessment, detailed site characterization and begin the permitting process. These efforts are designed to lead to commercial scale operations that demonstrate that long-term capture and storage can be performed safely and securely. This presentation will include the latest updates from the CarbonSAFE Initiative.
Webinar #35: HPC and AI/ML for Subsurface Applications
Speaker: Masashi Ikuta, NEC Corporation
The presentation will start from introducing the unique vector computer we have today. The speaker will tell you what is vector and how it can address subsurface applications. The presentation will refer to some benchmarking efforts with some performance numbers.
Webinar #33: History Matching High Resolution Geologic Models: Some Recent Advances
Speaker: Dr. Akhil Datta-Gupta
History Matching High Resolution Geologic Models: With the advances in subsurface characterization and imaging, petroleum reservoir models now-a-days routinely consist of multimillion cells representing geologic heterogeneity. Reconciling such high-resolution geologic models to dynamic data such as transient pressure, tracer, multiphase production history, and time-lapse seismic data is by far the most time-consuming aspect of the workflow for geoscientists and engineers.
Webinar #31: Fluid Monitoring Using Electromagnetics and Cloud/Artificial Intelligence: The devil is in the details
Speaker: Kurt Strack
Reservoir monitoring to image fluid movement has long been very challenging, not only because of the technological hurdles but also because of the business model. To realize its highest value, it is important to be able to provide results as close as possible to real time to influence operational decisions.
Over the last 20 years we developed a system of technologies to address the challenges in methodology, instruments, 3D modeling and data delivery. After numerous 3D feasibilities and several pilots, we are now able to receive the data from our instruments in near real time from anywhere in the world while keeping all data synchronized.
Electromagnetics is especially suited to directly image fluid movements in reservoirs. When we started our first 3D feasibility studies over 20 year ago, we realized that neither equipment nor software available was sufficient. Today, being able to carry out a full survey, we start out a 3D modeling feasibility study for either EOR or CCS applications and combine it with local noise measurements to design the optimum electromagnetic array which is then deployed in the field. Since electromagnetic sensors are sensitive to radio/cell phone signals, sending data in real time to the Cloud is tricky but is achievable. Next, the 3D modeling algorithm gets replaced with Artificial Intelligence (AI) to provide 3D models close to real time. Our ultimate vision is to do all of acquisition, processing and 3D imaging in real time directly from a cell phone.
Webinar #19: CASERM – an Industry/Agency/Academic Research Center for Subsurface Resource Science
Speakers: Dr. Thomas Monecke and Dr. Erik Westman
CASERM is an NSF-funded I/UCRC with the purpose of developing fundamental knowledge that transforms the way geoscience data is used to locate and characterize subsurface earth resources. Work within the center leverages expertise in geology, data science, and mining engineering to integrate diverse geoscience data, inform decision making, and minimize geological risk. Although less than two years old, the center is supported by seven members including Rio Tinto, AngloGold Ashanti, and the USGS. This presentation will introduce the center, it’s objectives, and expertise available.
Webinar #18: A Case Study for Optimizing Shut-In Strategies During Hydraulic Fracturing Operations in the Bakken
Speaker: Dr. Ali Shahkarami
Data science techniques have proven useful with the high volume of data collected in unconventional reservoir development workflows. In this paper, we present an analytics and machine learning use case for operations to minimize deferred production and quantify long-term production impacts due to frac hits in the Bakken and Three Forks formation during infill development.
The use case applied a workflow to a large field dataset. We underscore that historical data can be used to quantify the zonal communication and to provide recommendations for future operations regarding a shut in radius. With this novel approach, we analyzed several well pads in Bakken basin and all in close proximity. The analysis included the following datasets: static geological/formation data, completion data, one-second pressure data, and production history. The method used in this study can be defined as a 3-step process: 1) Employing analytics to assess and evaluate fracture driven interferences during the completion of new infill wells. 2) Quantifying the long-term production impact that may occur after shutting an offset well. 3) Applying machine learning techniques to determine the optimal offset distance and degree of communication.
Pressure data from the offset monitoring wells were used to determine the presence of fracture driven communication between wells during a completion operation. Production data were also utilized to quantify the long-term impact of shut in and fracture driven interferences. Machine learning techniques were then applied to measure the influence of offset distance (and other parameters such as completion design, depletion history, and zonal variance) to communication. The results of the analysis indicated the distance at which communication occurs most often in offset wells from the hydraulic completion of new infill wells. Considering this information, an optimized shut in distance was proposed for offset wells in the area reducing it from the previous radius by 250-550 ft thus improving production metrics.
Webinar #17: Harnessing the Power of DOE Data Computing for End-user Analytics
Speakers: Kelly Rose, Aaron Barkhurst, MacKenzie Mark-Moser, Lucy Romeo, and Patrick Wingo
The Offshore Risk Modeling (ORM) suite is comprised of innovative science- and data-driven computational tool and models designed to predict, prevent, and prepare for future oil spills. This R&D 100 award-winning suite of models, tools, and associated big-datasets span the full engineered and natural offshore system – from the subsurface, through the water column, and to the coast.
Webinar #15: IOT, Real-Time Analytics and Machine Learning: An Operator’s Perspective
Speaker: Dingzhou Cao, WPX Energy
Digital transformation is a buzzword within oil and gas upstream for a while. In this webinar, the author would discuss the real-time analytics systems he built within Anadarko and the similar system he is building within WPX right now, regarding the technical details, the pros and cons of each systems, as well as the analytics models (including machine learning models) et al.
Webinar #14: Fusion of Physics with Analytics for Oil and Gas
Speaker: Dr. Sathish Sankaran, Xecta Digital Labs
With ongoing digital transformation in the oil and gas industry, there is a significant surge in recent years in data collection and subsequently using data-driven models for fast computations. However, several of these applications spanning drilling, completions, reservoir and production engineering share a few common traits – limited extrapolation, less explainability and need for a lot of data (often expensive) for training.
Webinar #13: SEAM – SEG Collaborative Research – History and Way Forward
Speakers: Dr. Josef Paffenholz and Dr. Michael Oristaglio
Former chair of the SEAM board of directors Josef Paffenholz and project manager Mike Oristaglio will talk about the history, administrative structure, technical accomplishments so far and future plans of the SEG Advanced Modeling (SEAM) corporation.
Webinar #12: What Hath Reverend Bayes Wrought: Powerful Probabilistic Inference on Your Laptop
Speakers: Dennis Buede, Ph.D. and Joseph Tatman, Ph.D.
Bayesian networks are an algorithmic implementation for (1) defining a joint probability distribution and (2) engaging in both probabilistic and Bayesian inference as new information becomes available. This webinar introduces an exemplary Bayesian network, touches on the underlying mathematics, and provides some real-world examples. The real-world examples are diagnosing equipment malfunctions and classifying tunnels.
Webinar #11: Evaluating Variable Importance in Black Box Models: A Comparison of Strategies
Speaker: Dr. Jared Schuetter, Battelle
Data-driven models built using machine learning techniques are increasingly being used for many oil and gas applications, e.g., geologic characterization, drilling optimization, production data analysis, reservoir management, predictive maintenance, etc. Because of the “black box” nature of these models, an explicit relationship cannot generally be extracted between the input variables (predictors) and output variables (responses).
Webinar #10: Physics-informed Machine Learning for Real-time Unconventional Reservoir Management
Speaker: Dr. Hari Viswanathan
We present a physics-informed machine learning (PIML) workflow for real-time unconventional reservoir management. Reduced-order physics and high-fidelity physics model simulations, lab-scale and sparse field-scale data, and machine learning (ML) models are developed and combined for real-time forecasting through this PIML workflow.
Webinar #8: Machine Learning Reveals Surprising Findings
Speaker: Dr. Paul Johnson, Los Alamos National Lab
Our recent work with applying machine learning tools to geophysical data sets is leading to remarkable results. Seismic signals we once regarded as noise turn out to be the most important signal in the system regarding probing fault physics—revealing far more information about the instantaneous and future behavior of faults.
Webinar #4: Top-Down Modeling – Data-Driven Reservoir Simulation & Modeling using AI and Machine Learning
Speaker: Dr. Shahab D. Mohaghegh, WVU
To efficiently develop and operate a petroleum reservoirs, it is important to have a model. Currently, numerical reservoir simulation is the accepted and widely used technology for this purpose. Data-Driven Reservoir Modeling (Also known as Top-Down Modeling or TDM) is an alternative to numerical simulation.