Webinars

To view SMART Team Access-Only Webinars, click here (Must be part of private workspace on EDX).

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. 

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.   

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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).

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.

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.

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.