The Leading Edge最新文献

{"title":"Memorial","authors":"Ö. Yilmaz","doi":"10.1190/tle43060404.1","DOIUrl":"https://doi.org/10.1190/tle43060404.1","url":null,"abstract":"Sven Treitel","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":"54 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141277288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel approaches to uncertainty estimation in seismic subsurface characterization","authors":"Matt Walker, Alistair Crosby, Angus Lomas, Eric Kazlauskas, Reetam Biswas, Pedro Paramo, Kevin Wolf, Madhav Vyas","doi":"10.1190/tle43060347.1","DOIUrl":"https://doi.org/10.1190/tle43060347.1","url":null,"abstract":"Uncertainty estimation in subsurface characterization workflows is an important input to decision-making in earth science-related problems. We present three methods to characterize seismic-related uncertainty, each of which includes a real data case study. Two of these methods are designed to characterize depth uncertainty in positioning of migrated reflectors. Such estimates may be used in derisking well depth prognoses, analysis of well misties, and for estimating ranges on resource volume. The first method derives rapid and robust estimates of depth uncertainty around an existing velocity model using traditional velocity analysis to constrain the solution space while limiting a-priori constraints, consistent with a frequentist approach to uncertainty characterization. The second method characterizes depth uncertainty more rigorously in respect to the physics and prior information available by performing full-waveform inversion in a Bayesian framework. This method produces similar uncertainty estimates to the first in the case of simple velocity models but is more accurate where the overburden is complex; however, it requires significantly greater computational expense, thus limiting current practical applications. The third method is an amplitude variation with angle (AVA) inversion for reservoir properties designed to output uncertainty products for interpreters, utilizing the Bayesian framework and Zoeppritz equations to define the forward physics. Application to an offshore Egypt field demonstrates that it can generate reliable estimates of reservoir properties (e.g., lithofluid type or shale volume) including uncertainty, useful in various parts of subsurface characterization. These results also show that the method could provide improved point estimates of reservoir properties compared to conventional deterministic AVA inversion approaches. There is usually a trade-off between increasing the accuracy of subsurface characterization versus creating faster, less expensive, and more readily understood workflows for practitioners. We discuss how the relative importance of these competing factors should be considered within the context of how the outputs will be used.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":"38 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141280310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Invertible neural networks for uncertainty quantification in refraction tomography","authors":"Yen Sun, Paul Williamson","doi":"10.1190/tle43060358.1","DOIUrl":"https://doi.org/10.1190/tle43060358.1","url":null,"abstract":"Uncertainty quantification (UQ) should be an essential ingredient of geophysical inversion because it measures the confidence in the results and enables the assessment of the value of information in the data. However, UQ using established methods ranges from very expensive to prohibitively costly, and estimating noise levels and integrating prior information is challenging, so it is not yet widely undertaken. In this paper, we explore the capabilities of a machine learning-based UQ tool known as the invertible neural network (INN) and focus on its application to a 2D tomography problem within a complex foothills environment. We propose a novel approach to handle realistic problem dimensions that uses variational autoencoders to compress the velocity model and data. The INN relates the respective latent spaces, significantly reducing memory requirements. Our findings reveal that this INN-based workflow can perform tomographic inversion while integrating an implicit prior in the form of a set of velocity models with pertinent features. Furthermore, we can address both epistemic and aleatoric uncertainties by adopting a deep ensemble strategy. This integrated approach yields plausible estimates of relative confidence in the inverted velocities, showcasing the potential of INN as a tool for UQ in geophysical inversion.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to this special section: Subsurface uncertainty","authors":"Madhav Vyas, David Lubo-Robles, Matt Walker","doi":"10.1190/tle43060336.1","DOIUrl":"https://doi.org/10.1190/tle43060336.1","url":null,"abstract":"Handling the nonuniqueness and ambiguity of geophysical inversion results is a major challenge in characterizing the subsurface. Geophysical inversion is the process of estimating the physical properties of the subsurface from the measured geophysical data, such as seismic, electromagnetic, gravity, or magnetic signals. However, there are often many different subsurface models that can fit the same data adequately well. Therefore, it is important to evaluate the uncertainty and reliability of the inverted models and integrate other sources of information, such as geologic, petrophysical, or geochemical data, to reduce uncertainty. Despite the industry's advances in recent decades, the number of subsurface outcomes that fall outside predicted ranges is disproportionate to the supposed certainty of those ranges. This leads to inefficient allocation of investment budgets, which is particularly painful due to the capital-intensive nature of the oil and gas industry.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":"64 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141277115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global geologic expert system and database for predicting carbonate reservoirs from seismic: Application to the Upper Jurassic","authors":"Omar Nacef, Julien Michel, Jean Borgomano, Bertrand Martin-Garin","doi":"10.1190/tle43060382.1","DOIUrl":"https://doi.org/10.1190/tle43060382.1","url":null,"abstract":"Carbonate systems are influenced by a great variety of physical and biological controlling factors that operate from global to local scales. The resulting intrinsic complexity of carbonate platforms makes them difficult to predict, especially when data are limited. Predicting geologic geometries and properties based on limited sampling or uncalibrated seismic data generally relies on a priori knowledge and equivocal interpretations that are marked by geologist perception and personal experience. To overcome these uncertain interpretations of such a complex natural system, which can become critical in frontier exploration, we developed an expert system that relies on a process-based method and a standardized data set using normalized information and parameters. The main innovation relies on the realization of knowledge- and process-based synthetic carbonate stratigraphic architectures that support seismo-stratigraphic interpretations. The workflow consists of four steps: (1) bibliographic compilation of a geologic database for each case study supported by quantitative parameters (e.g., sedimentation duration and thickness) and qualitative parameters (geodynamic context, seismic architecture, and facies model); (2) statistical analyses to establish consistent geologic classes and spatiotemporal trends; (3) process-based modeling to simulate stratigraphic architectures associated with carbonate sedimentation processes in a physically constrained numerical environment and testing different geologic hypotheses; and (4) realization of a predictive palaeogeographic map representing the global distribution of carbonate stratigraphic architectures, and estimation of controlling parameters for unconstrained case studies. The expert system is based on 77 case studies of Upper Jurassic carbonate platforms, which reveal the resemblance of these carbonate systems, in response to uniform global palaeoclimatic conditions and sea level. Significant local differences in stratigraphic architectures are related to specific geodynamic contexts and subsidence trends. The thickest carbonate platforms are developed in extensive/passive geodynamic settings such as the Central Atlantic Ocean margins, while thinner platforms form in intra- and peri-cratonic settings such as those of the Arabian region.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141278495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Calendar","authors":"","doi":"10.1190/tle43060333.1","DOIUrl":"https://doi.org/10.1190/tle43060333.1","url":null,"abstract":"The Editorial Calendar details upcoming publication plans for The Leading Edge. This includes special sections, guest editors, and information about submitting articles to TLE.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":"13 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geoscientists Around the Globe","authors":"","doi":"10.1190/tle43060400.1","DOIUrl":"https://doi.org/10.1190/tle43060400.1","url":null,"abstract":"Coordinated by members of SEG's Justice, Equity, Diversity, and Inclusion (JEDI) Committee, TLE's Geoscientists Around the Globe department features geoscientists from technically, geographically, and culturally diverse backgrounds. In this installment, JEDI Committee member Ellie Ardakani interviews Teyyuba Adigozalova, a geophysicist at bp.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic Soundoff: The secret to succeeding as a teacher","authors":"Andrew Geary","doi":"10.1190/tle43060408.1","DOIUrl":"https://doi.org/10.1190/tle43060408.1","url":null,"abstract":"Roel Snieder discusses how to excel as a teacher (and professional) by using the Teaching with Heart practices. He discusses the importance of creating a more nurturing and loving educational environment. He also addresses the potential pitfalls of ego in teaching, the importance of seeing students as individuals with unique challenges and aspirations, and the delicate balance of maintaining professional boundaries while cultivating meaningful relationships.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":"36 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141276107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized rejection sampling for estimating facies probabilities from seismic data","authors":"Patrick Connolly, B. Dutton","doi":"10.1190/tle43060368.1","DOIUrl":"https://doi.org/10.1190/tle43060368.1","url":null,"abstract":"Seismic inversion for facies has nonunique solutions. There are invariably many vertical facies arrays that are consistent with both a data trace and the prior information. Stochastic sampling algorithms set within a Bayesian framework can provide an estimate of the posterior probability distribution of facies arrays by finding the arrays with relatively high posterior probabilities for each data trace. Sample-by-sample facies probabilities can be estimated by measuring the proportions of each facies type at each sample location from the set of posterior facies arrays. To enable the estimation of probabilities of facies mixtures and to obtain high-quality images of facies probability curves, facies must be modeled at high resolution. The facies arrays, or vectors, on which the sampling algorithm operates, must also be long enough to allow for vertical coupling caused by the wavelet. This results in very large sample spaces. The posterior probability distribution is highly nonconvex, which, combined with the large sample space, severely challenges conventional stochastic sampling methods in obtaining convergence of the estimated posterior distribution. The posterior sets of vectors from conventional methods tend to be either correlated or have low predictabilities, resulting in biased or noisy facies probability estimates, respectively. However, accurate estimates of facies probabilities can be obtained from a relatively small number of posterior facies vectors (about 100), provided that they are uncorrelated and have high predictabilities. Full convergence of the posterior distribution is not required. A hybrid algorithm optimized rejection sampling can be designed specifically for the seismic facies probability inversion problem by combining independent sampling of the prior, which ensures posterior vectors are uncorrelated, with an optimization step to obtain high predictabilities. Tests on both real and synthetic data demonstrate better results than conventional rejection sampling and Markov chain Monte Carlo methods.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":"68 52","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141276639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic data — What am I looking at?","authors":"Martin P. Bates, Edward K. Biegert, Alan B. Reid","doi":"10.1190/tle43040210.1","DOIUrl":"https://doi.org/10.1190/tle43040210.1","url":null,"abstract":"For a number of years in geophysical surveying, the use of certain technical terms that describe data has not been consistent. This is particularly apparent in the field of magnetic surveying, which is the most commonly practiced technique. Across the world, there are multiple companies and clients that collect and employ magnetic data acquired via ground, airborne, or marine platforms. However, what they mean by certain terms is either imprecisely defined, ambiguous, or significantly different. Terms that are accurate and consistent will help the end user understand what they are actually looking at. We describe the most commonly used terms for magnetic data and discuss how these terms should and should not be used.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":"749 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140790901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}