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saturation
Lucy MacGregor is a leading researcher in multi-physics analysis with particular expertise in the integration of electromagnetic methods into reservoir characterization workflows. She served as SEG Honorary Lecturer in Europe in 2011 and as Distinguished Lecturer in 2021. Lucy has a PhD from the University of Cambridge for research in the field of controlled-source electromagnetic (CSEM) methods and over 25 years of experience in marine EM surveying and its application to the detection and characterization of fluids in the earth. Following her PhD, she was a Green Scholar at the Scripps Institution of Oceanography working on marine electromagnetic methods, before returning to Cambridge as a Leverhulme Trust/Downing College research fellow. In 2000 she moved to the National Oceanography Centre, Southampton as a NERC research fellow to continue her work, and she took part in the first survey targeting CSEM at hydrocarbon reservoirs.
- Information Technology > Knowledge Management (0.76)
- Information Technology > Communications > Collaboration (0.76)
We present a new alternative for the joint inversion of well logs to predict the volumetric and zone parameters in hydrocarbon reservoirs. Porosity, water saturation, shale content, kerogen and matrix volumes are simultaneously estimated with the tool response function constants with a hyperparameter estimation assisted inversion of the total and spectral natural gamma-ray intensity, neutron porosity and resistivity logs. We treat the zone parameters, i.e., the physical properties of rock matrix constituents, shale, kerogen, and pore-fluids, as well as some textural parameters, as hyperparameters and estimate them in a meta-heuristic inversion procedure for the entire processing interval. The selection of inversion unknowns is based on parameter sensitivity tests, which show the automated estimation of several zone parameters is favorable and their possible range can also be specified in advance. In the outer loop of the inversion procedure, we use a real-coded genetic algorithm for the prediction of zone parameters, while we update the volumetric parameters in the inner loop in addition to the fixed values of zone parameters estimated in the previous step. We apply a linearized inversion process in the inner loop, which allows for the quick prediction of volumetric parameters along with their estimation errors from point to point along a borehole. Derived parameters such as hydrocarbon saturation and total organic content show good agreement with core laboratory data. The significance of the inversion method is in that zone parameters are extracted directly from wireline logs, which both improves the solution of the forward problem and reduces the cost of core sampling and laboratory measurements. In a field study, we demonstrate the feasibility of the inversion method using real well logs collected from a Miocene tight gas formation situated in the Derecske Trough, Pannonian Basin, East Hungary.
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.71)
- Europe > Slovakia > Pannonian Basin (0.99)
- Europe > Serbia > Pannonian Basin (0.99)
- Europe > Romania > Pannonian Basin (0.99)
- (9 more...)
Petroleum Engineering, University of Houston, 2. Metarock Laboratories, 3. Department of Earth and Atmospheric Sciences, University of Houston) 16:00-16:30 Break and Walk to Bizzell Museum 16:30-17:30 Tour: History of Science Collections, Bizzell Memorial Library, The University of Oklahoma 17:30-19:00 Networking Reception: Thurman J. White Forum Building
- Research Report > New Finding (0.93)
- Overview (0.68)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Mineral (0.72)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- (2 more...)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
Rock-physics model of a gas hydrate reservoir with mixed occurrence states
Wu, Cun-Zhi (China University of Petroleum (Beijing)) | Zhang, Feng (China University of Petroleum (Beijing)) | Ding, Pin-Bo (China University of Petroleum (Beijing)) | Sun, Peng-Yuan (CNPC, National Engineering Research Center for Oil and Gas Exploration Computer Software) | Cai, Zhi-Guang (CNPC, National Engineering Research Center for Oil and Gas Exploration Computer Software) | Di, Bang-Rang (China University of Petroleum (Beijing))
ABSTRACT Seismic interpretation of gas hydrates requires the assistance of rock physics. Changes in gas hydrate saturation can alter the elastic properties of formations, and this relationship can be considerably influenced by the occurrence state of gas hydrates. Pore-filling, load-bearing, and cementing types are three single gas hydrate occurrence states commonly considered in rock-physics investigations. However, many gas hydrate-bearing formations are observed to have mixed occurrence states, and their rock-physics properties do not fully conform to models of single occurrence states. We develop a generalized rock-physics model for gas hydrate-bearing formations with three mixed occurrence states observed in the field or laboratory experiments: coexisting pore-filling-type and matrix-forming-type gas hydrates (case 1); pore-filling type when (gas hydrate saturation) < (critical saturation) and pore-filling + matrix-forming type when (case 2); and matrix-forming type when and matrix-forming + pore-filling type when (case 3). Instead of initial porosity, the apparent porosity (the volume fraction of an effective pore filler) represents the influence of occurrence states on the pore space. These three mixed occurrence states can be modeled using a unified workflow, in which the volume fractions of various gas hydrate types are expressed in general forms in terms of the apparent porosity. In addition, the model considers the effect of a pore filler on shear modulus. The developed model is validated through calibration with real well-log data and published experimental data corresponding to five gas hydrate-bearing formations. The model effectively interprets the influences of gas hydrate saturation and occurrence state on these formations. Thus, the generalized model provides a theoretical basis for the analysis of sensitive elastic parameters and quantitative interpretation for gas hydrate reservoirs.
- North America > United States (0.46)
- Asia > China (0.29)
- Asia > Middle East > Israel > Mediterranean Sea (0.24)
- Europe > Norway > Norwegian Sea (0.24)
Deep carbonate reservoir characterization using multiseismic attributes: A comparison of unsupervised machine-learning approaches
Zhao, Luanxiao (Tongji University) | Zhu, Xuanying (Tongji University) | Zhao, Xiangyuan (SINOPEC, Petroleum Exploration and Production Research Institute) | You, Yuchun (SINOPEC, Petroleum Exploration and Production Research Institute) | Xu, Minghui (Tongji University) | Wang, Tengfei (Tongji University) | Geng, Jianhua (Tongji University)
ABSTRACT Seismic reservoir characterization is of great interest for sweet spot identification, reservoir quality assessment, and geologic model building. The sparsity of the labeled samples often limits the application of supervised machine learning (ML) for seismic reservoir characterization. Unsupervised learning methods, in contrast, explore the internal structure of data and extract low-dimensional features of geologic interest from seismic data without the need for labels. We compare various unsupervised learning approaches, including the linear method of principal component analysis (PCA), the manifold learning methods of t-distributed stochastic neighbor embedding and uniform manifold approximation and projection (UMAP), and the convolutional autoencoder (CAE), on the 3D synthetic and field seismic data of a deep carbonate reservoir in southwest China. On the synthetic data, the low-dimensional features extracted by UMAP and CAE provide a better indication of porosity and gas saturation than traditional seismic attributes. In particular, UMAP better preserves the global structure of geologic features and indicates the potential of decoupling the gas saturation and porosity effects from seismic responses. We demonstrate that joint use of several types of seismic attributes, instead of using a single type of seismic attributes, can better delineate the reservoir structures using unsupervised ML. On the field seismic data, UMAP can effectively characterize the sedimentary facies distribution, which is consistent with the geologic understanding. Nevertheless, the porosity and saturation can not be reliably identified from field seismic data using unsupervised ML, which is likely caused by the complex pore structures in carbonates complicating the mapping relationship between seismic responses and reservoir parameters.
- North America > United States > Texas > Yoakum County (0.75)
- North America > United States > Louisiana (0.75)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (1.00)
- Geology > Geological Subdiscipline (0.93)
- South America > Brazil > Brazil > South Atlantic Ocean > Santos Basin (0.99)
- North America > Mexico > Veracruz > Veracruz Basin (0.99)
- North America > Mexico > Gulf of Mexico > Veracruz Basin (0.99)
- Asia > China > Sichuan > Sichuan Basin (0.99)
- Information Technology > Artificial Intelligence > Machine Learning > Unsupervised or Indirectly Supervised Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.88)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.46)
ABSTRACT We describe, implement, and show the results of a localized ensemble-based approach for seismic amplitude-variation-with-offset (AVO) inversion with uncertainty quantification. Ensembles are simulated from prior probability distributions for fluid saturations and clay content. Starting with continuous saturations and clay content variables, we use depth-varying models for cementation and grain contact theory, Gassmann fluid substitution with mixed saturations, and approximations to the Zoeppritz equations for the AVO attributes at the top-reservoir. The local conditioning to seismic AVO observations relies on (1) the misfit between ensemble simulated seismic AVO data and the field observations in a local partition of the grid/local patch, of inlines/crosslines around the locations where we aim to predict, (2) correlations between the simulated reservoir properties and the data in local patches, and (3) local assessment to avoid unrealistic updates based on spurious correlations in the ensembles. Data from the Alvheim field in the North Sea are used to demonstrate the approach. The influence of the prior information from the well logs in combination with the seismic reflection data indicates the presence of higher oil and gas saturation in the lobe structures of the field and increased clay content at their edges.
- Geology > Mineral > Silicate > Phyllosilicate (0.93)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.93)
- Geology > Geological Subdiscipline > Geomechanics (0.84)
- Europe > Norway > North Sea > Northern North Sea > South Viking Graben > Vana Basin > RL 088 BS > Block 25/4 > Alvheim Field > Lista Formation > Våle Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > South Viking Graben > Vana Basin > RL 088 BS > Block 25/4 > Alvheim Field > Lista Formation > A2 North Heimdal T60 Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > South Viking Graben > Vana Basin > RL 088 BS > Block 25/4 > Alvheim Field > Hermod Formation > Våle Formation (0.99)
- (25 more...)
Induced polarization of clay-rich materials. 3. Partially-saturated mixtures of clay and pyrite
Revil, Andr (EDYTEM) | Ghorbani, A. (Naga Geophysics) | Su, Zhaoyang (China University of Petroleum (Beijing)) | Cai, Hongzhu (China University of Geosciences, China University of Geosciences) | Hu, Xiangyun (China University of Geosciences, China University of Geosciences)
Source rocks for oil and gas are often associated with shales that are rich in pyrite and kerogen. Induced polarization is a suitable tool to characterize these formations. We develop a new experimental database of 43 laboratory experiments using mixtures of clay and pyrite under fully or partially water-saturated conditions. The liquid water saturation is in the range of 20%–100%, whereas the pyrite content is in the range of 0%–17%. Spectral induced polarization measurements are performed in the frequency range of 0.1 Hz to 45 kHz at room temperature (approximately 25C 1C). The complex conductivity spectra are fitted with a Cole-Cole model and the Cole-Cole parameters are determined using a stochastic procedure based on a Markov chain Monte Carlo sampler. The Cole-Cole parameters associated with the low-frequency dispersion are then plotted as a function of the (water) saturation and pyrite content (volume fractions). Four predictions of the model are tested against the experimental data. We find that the model is able to explain the results including (1) the dependence of the chargeability with the pyrite content, (2) the (instantaneous) conductivity depends on the saturation according to an Archies law, (3) the Cole-Cole exponent does not depend on saturation and pyrite content (except at very small pyrite content of <1 vol%), and (4) the relaxation time is inversely proportional to the instantaneous conductivity. We also develop a more advanced petrophysical model using a double Cole-Cole distribution, in which one distribution is associated with the clay minerals and the other is associated with the pyrite.
- Geology > Mineral > Sulfide (1.00)
- Geology > Mineral > Silicate > Phyllosilicate (0.71)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.34)
- Materials > Metals & Mining (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (0.91)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.77)
Participation in over 150 carbon capture and sequestration (CCS) projects spanning more than 25 years has led to the evolution of a recommended well-based appraisal workflow for CO2 sequestration in saline aquifers. Interpretation methods are expressly adapted for CCS applications to resolve key reservoir parameters, constrain field-scale modeling, provide answers required for the permitting process, and derisk the three principal CCS evaluation challenges: storage capacity, injectivity, and containment. Each of these may be further complicated by eventual three-way interaction between rock matrix, brine, and CO2 streams. Many relevant logging and core analysis techniques for CCS may be borrowed or adapted from oil and gas exploration and other related industries, but acquiring, interpreting, and putting the data to optimum use requires an entirely new mindset. We highlight seven specific technical challenges of CCS appraisal that make it completely different from, and much more challenging than conventional oil and gas appraisal. For example, in hydrocarbon exploration, petrophysicists have become adept at acquiring and interpreting downhole measurements to quantify existing fluid saturations.
Machine-learning application to assess occurrence and saturations of methane hydrate in marine deposits offshore India
Chong, Leebyn (National Energy Technology Laboratory, NETL Support Contractor) | Collett, Timothy S. (U.S. Geological Survey) | Creason, C. Gabriel (National Energy Technology Laboratory) | Seol, Yongkoo (National Energy Technology Laboratory) | Myshakin, Evgeniy M. (National Energy Technology Laboratory, NETL Support Contractor)
Abstract Artificial neural networks (ANN) were used to assess methane hydrate occurrence and saturation in marine sediments offshore India. The ANN analysis classifies the gas hydrate occurrence into three types: methane hydrate in pore space, methane hydrate in fractures, or no methane hydrate. Further, predicted saturation characterizes the volume of gas hydrate with respect to the available void volume. Log data collected at six wells, which were drilled during the India National Gas Hydrate Program Expedition 02 (NGHP-02), provided a combination of well-log measurements that were used as input for machine-learning (ML) models. Well-log measurements included density, porosity, electrical resistivity, natural gamma radiation, and acoustic wave velocity. Combinations of well logs used in the ML models provide good overall balanced accuracy (0.79 to 0.86) for the prediction of the gas hydrate occurrence and good accuracy (0.68 to 0.92) for methane hydrate saturation prediction in the marine accumulations against reference data. The accuracy scores indicate that the ML models can successfully predict reservoir characteristics for marine methane hydrate deposits. The results indicate that the ML models can either augment physics-driven methods for assessing the occurrence and saturation of methane hydrate deposits or serve as an independent predictive tool for those characteristics.
- Geology > Sedimentary Geology > Depositional Environment > Marine Environment (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.46)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > North America Government > United States Government (0.68)
- Asia > India > Andhra Pradesh > Bay of Bengal > Krishna-Godavari Basin (0.99)
- North America > United States > Alaska (0.89)
- North America > Canada (0.89)
Experimental seismic crosshole setup to investigate the application of rock physical models at the field scale
Birnstengel, Susann (Helmholtz Centre for Environmental Research) | Dietrich, Peter (Helmholtz Centre for Environmental Research) | Peisker, Kilian (Helmholtz Centre for Environmental Research) | Pohle, Marco (Helmholtz Centre for Environmental Research) | Hornbruch, Gtz (Christian Albrechts Universitt zu Kiel) | Bauer, Sebastian (Christian Albrechts Universitt zu Kiel) | Hu, Linwei (Christian Albrechts Universitt zu Kiel) | Gnther, Thomas (Leibniz Institute for Applied Geophysics) | Hellwig, Olaf (TU Bergakademie Freiberg) | Dahmke, Andreas (Christian Albrechts Universitt zu Kiel) | Werban, Ulrike (Helmholtz Centre for Environmental Research)
Seismic crosshole techniques are powerful tools to characterize the properties of near-surface aquifers. Knowledge of rock-physical relations at the field scale is essential for interpreting geophysical measurements. However, it remains difficult to extend the results of existing laboratory studies to the field scale due to the usage of different frequency ranges. To address this, we develop an experimental layout that successfully determines the dependency of gas saturation on seismic properties. Integrating geophysical measurements into a hydrogeological research question allows us to prove the applicability of theoretical rock physical concepts at the field scale, filling a gap in the discipline of hydrogeophysics. We use crosshole seismics to perform a time lapse study on a gas injection experiment at the TestUM test site. With a controlled two-day gaseous CH4 injection at 17.5 m depth, we monitor the alteration of water saturation in the sediments over a period of twelve months, encompassing an observational depth of 813m. The investigation contains an initial P-wave simulation followed by a data-based P-wave velocity analysis. Subsequently, we discuss different approaches on quantifying gas content changes by comparing Gassmanns equation and the time-average relation. With the idea of patchy saturation, we discover that analyzing P-wave velocities in the subsurface is a suitable method for our experiment, resulting in a measurement accuracy of 0.2 vol.%. We demonstrate that our seismic crosshole setup is able to describe the relation of the rocks elastic parameter on modified fluid properties at the field scale. With this method, we are able to quantify relative water content changes in the subsurface.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (1.00)