Subsurface Insights (United States)

ABSTRACT Pre‐ and post‐stack seismic inversion is the primary approach for converting collected seismic data into geophysical property models particularly velocity for subsurface interpretation and reservoir characterization. The traditional workflows often start from an initial property model and iteratively revise it by minimizing the misfit between the acquired real seismic dataset and the synthetic one derived from the updated property models, here denoted as the soft constraint in seismic space. However, it heavily relies on human supervision in building a good initial model and monitoring the misfit optimization process. On the contrary, most of the recent deep learning‐based workflows target at non‐linearly mapping seismic patterns to properties measured at available well only, here denoted as the hard constraint in property space. Correspondingly, its accuracy greatly depends on the availability of sufficient training wells; otherwise, overfitting occurs causing the machine prediction not meeting the soft constraint throughout the target seismic survey. To resolve these limitations, this study presents a practical workflow that enables rock property inversion from partial‐stack seismic via two physics‐guided convolutional neural networks (CNNs), with the first one embedding the approximated AVO gradient to build initial property models that satisfy the hard constraint and the second one embedding the reflection coefficients to refine the models by enforcing the soft constraint. Between both CNNs is the use of well‐established relevant physics to generate pseudo property–reflectivity–seismic pairs for training the second CNN. Its added values are validated through applications to the Volve survey in North Sea and the Exmouth survey in Western Australia. The produced property volumes not only are observed of high lateral consistency and vertical resolution but also derive synthetic seismic data that are closely correlated with actual seismic data.

Abstract Electromagnetic (EM) irradiation is a relatively nascent, but promising oil recovery method. It is well-positioned to enable carbon-free oil production from heavy oil- and bitumen-rich formations. Unfortunately, this method has not been widely adopted yet due to a lack of understanding of the dielectric properties of oil-bearing rocks at elevated thermobaric conditions. The present work aims to bridge this gap by conducting dielectric property frequency spectra measurements of heated and pressurized "native" core samples. A patented system was commissioned and a series of dielectric measurements of saturated oil sand core samples were performed in two successive heating-cooling cycles within the 20°C to 220°C range. The pore pressure was kept constant above steam saturation point for the first cycle and was purged by the second cycle's end to induce evaporation. Moreover, every measured sample was compacted by a load of 1000 psi (6.89 MPa). In addition, a comprehensive database management system was designed to facilitate automatic data acquisition of the time- and temperature-series of the relative dielectric constant and conductivity frequency responses. The relative dielectric constant and the conductivity frequency spectra were found to be directly proportionate to temperature with some level of hysteresis between the heating and cooling cycle trends. To better visualize and interpret this behavior, three-dimensional maps of the dielectric properties in frequency-temperature domain were constructed. Singularities were observed in the results for the saturated clean and oil sand samples at certain resonant frequencies. Those frequencies signify extremums in the EM energy transfer to the formation. Increased values in the relative dielectric constant and decreased values in the conductivity at frequencies smaller than 1 MHz were attributed to the electrode polarization effect, which becomes more prominent at higher temperatures and concentration of dissolved solids. In all heating experiments of water saturated clean sands a full evaporation of the pore fluid was achieved, and the dielectric properties would approach the properties of air. However, no steam desiccated zones were observed in the oil sand samples. As a result, the local concentration of ions, minerals and other solids present in the pore fluid increased, which was further confirmed by the increase in the measured dielectric properties. To the best of the authors’ knowledge this was the first time the dielectric measurements of "native" oil sand sample cores that make up most heavy oil- and bitumen-bearing formations in Athabasca region, were performed in variable temperature-pressure-steam quality environments. These measurements should provide better means to modelling of electromagnetic energy dissipation in different parts of the formation and forecasting oil production by relevant EM heating methods.

Summary Subsurface Capture and Storage (SCS) projects are complex and span many decades, with secure storage required for centuries thereafter. Risk exposure is variable throughout project life and requires a robust workflow that allows for analysis of risk variations in time, including identification of “peak risk”. A workflow and tool is presented that can be applied to these projects. It comprises five steps: Project framing: Thorough framing of the project at the outset is needed to build a staged approach and establish success criteria. Definition of risking scales: An SCS project must include a quantitative assessment of both current and future risks, to evaluate the frequency of potential events and their impacts. Event and hazard identification: Applying the Features, Events and Processes (FEP) methodology facilitates building a comprehensive risk register. Quantification of long-term risk: Utilizing the Poisson formula and Monte Carlo models, we can calculate the probability of events over specific stages of a project. Refinement of risks: Milestones should be identified to enable conformance assessment during injection and monitoring. A thorough quantitative risk assessment enables proactive risk management to facilitate project success and minimize both the chance and impact of failures.

Crosshole seismic tomography is currently one of the reliable methods used in identifying the seismic velocity patterns for the development in areas challenged by presence of karstic features due to dissolution of the underlying limestone bedrock formation. The crosshole seismic tomography which provides a persistent image of the borehole and its surroundings in the form of P-wave velocity, picks up in-depth information compared to other surface geophysical methods. A crosshole seismic survey which consists of 99 lines connecting a total of 48 boreholes were conducted at Jalan Cochrane for a proposed residential development to determine the subsurface geology of the area. The site was divided into four main towers, namely Tower A and B (East) and Tower A and B (West). Jalan Cochrane is underlain by the Kuala Lumpur Limestone formation. This survey was mainly conducted to identify the possible cavities and incompetent rocks which are fractured and infilled in the proposed area. The crosshole seismic survey tomography correlated well with the borehole information obtained. Several drop in velocity observed were concluded to be possible cavities in the bedrock. Data obtained in Tower A and B (West) are more uniform and have a uniform bedrock level compared to the data in Tower A and B (East) which has an undulating bedrock level as well as fractured rocks and infilled cavities. This paper presents the findings obtained from the crosshole seismic survey conducted at Jalan Cochrane which successfully mapped out the cavities and bedrock level.

The stable water isotope data for the East River Watershed, Colorado, consists of delta2H (hydrogen) and delta18O (oxygen) values from samples collected at multiple, long-term monitoring sites including streams, groundwater wells, springs, and a precipitation collector used to establish a local meteoric water line (LMWL) for the watershed. These locations represent important and/or unique end-member locations for which stable isotope values can be diagnostic of the connection between precipitation inputs as snow and rain and riverine export. Such locations include drainages underline entirely or largely by shale bedrock, land covered dominated by conifers, aspens, or meadows, and drainages impacted by historic mining activity and the presence of naturally mineralized rock. Developing a long-term record of water isotope values from a diversity of environments is a critical component of quantifying the impacts of both climate change and discrete climate perturbations, such as drought, forest mortality, and wildfire, on water export. Such data may be combined with stream gaging stations co-located at each surface water monitoring site to relate seasonal variations in water export to their stable isotopic signature. Data for liquid water delta2H and delta18O values are reported in units of parts per thousand (per-mil; ‰). This data package contains (1) a zip file (isotope_data_2014-2024.zip) containing a total of 87 files: 86 data files of isotope data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (v5_20250515_flmd.csv) file that lists each file contained in the dataset with associated metadata; and (3) a data dictionary (v5_20250515_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type. Missing values within the anion data files are noted as either "-9999" or "0.0" for not detectable (N.D.) data. There are a total of 43 locations containing isotope data.Update on 2022-06-10: versioned updates to this dataset was made along with these changes: (1) updated isotope data for all locations up to 2021-12-31 and (2) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format.Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv).Update on 2023-08-08: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-03-13. The file level metadata and data dictionary files were updated to reflect the additional data added.Update on 2024-03-11: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2024-02-19. Further, revisions to the data files were made to remove incorrect data points (from 1970 and 2001). The reporting format specific files were updated to reflect the additional data added.Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available isotope data was added, up until the end of WY2024 (September 30, 2024). International Generic Sample Numbers (IGSNs), when registered, were added to the data files. The reporting format specific files were updated to reflect the additional data added.

Motivation: Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As `omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. Results: We propose a workflow (`Omics to Reactive Transport – ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBASE and PFLOTRAN. Live, interactive models, which incorporate the results from this narrative into a PFLOTRAN simulation, are available (without login) at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/.

Abstract For waterflood management and production optimization purposes, producers and injectors are often grouped into pattern elements based purely on the wells' location. This grouping is often done manually and can be time consuming. Also, for irregular patterns is it not always obvious which wells can be grouped together. The paper presents an algorithm that formalizes the grouping and automatically identifies pattern elements. The presented algorithm focuses on identification of pattern elements that contain one producer and several neighboring injectors. First, using the wells' coordinates as input data, Voronoi polygons are constructed. Based on the well types (producer or injector) and common edges of Voronoi polygons producer-injector pairs are identified. Pattern elements are constructed by splitting Voronoi polygons of injectors and adding their parts to Voronoi polygons of producers from correspondent wells pairs. In the resulting pattern elements injectors are located in one of the vertices while producers lie inside the element. The presented algorithm was tested on synthetic examples and proved to identify pattern elements for both regular (e.g. line drive, 5-spot, etc.) and irregular waterflood patterns. Therefore, it is suitable for automated analysis of waterflood patterns in cases where data or time constraints do not allow to implement streamline analysis or other sophisticated techniques. Calculation of voidage replacement for each identified pattern element allows to reveal areas of over-injection as well as areas lacking pressure support, and accordingly adjust rate targets for each injector in order to achieve and maintain a balanced waterflood. Automated identification of waterflood pattern elements helps to timely adjust water injection when new producers come on stream or existing wells shut-in. The proposed method provides a fast and pragmatic approach of identifying and updating patterns without performing dynamic simulation. Formalized steps of the presented algorithm allow automated identification of waterflood pattern elements. In addition to manual labor savings, it avoids subjectivity and ambiguity that can arise in case of irregular patterns.

Over the past three decades, the widespread utility and applicability of X-ray photoelectron spectroscopy (XPS) in research and applications has made it the most popular and widely used method of surface analysis. Associated with this increased use has been an increase in the number of new or inexperienced users which has led to erroneous uses and misapplications of the method. This article is the first in a series of guides assembled by a committee of experienced XPS practitioners that are intended to assist inexperienced users by providing information about good practices in the use of XPS. This first guide outlines steps appropriate for determining whether XPS is capable of obtaining the desired information, identifies issues relevant to planning, conducting and reporting an XPS measurement, and identifies sources of practical information for conducting XPS measurements. Many of the topics and questions addressed in this article also apply to other surface-analysis techniques.