[PDF] Monitoring Co2 Storage At Cranfield Mississippi With Time Lapse Offset Vsp U2013 Using Integration And Modeling To Reduce Uncertainty eBook

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Page : 9 pages
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Release : 2014
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A time-lapse Offset Vertical Seismic Profile (OVSP) data set was acquired as part of a subsurface monitoring program for geologic sequestration of CO2. The storage site at Cranfield, near Natchez, Mississippi, is part of a detailed area study (DAS) site for geologic carbon sequestration operated by the U.S. Dept. of Energy’s Southeast Regional Carbon Sequestration Partnership (SECARB). The DAS site includes three boreholes, an injection well and two monitoring wells. The project team selected the DAS site to examine CO2 sequestration multiphase fluid flow and pressure at the interwell scale in a brine reservoir. The time-lapse (TL) OVSP was part of an integrated monitoring program that included well logs, crosswell seismic, electrical resistance tomography and 4D surface seismic. The goals of the OVSP were to detect the CO2 induced change in seismic response, give information about the spatial distribution of CO2 near the injection well and to help tie the high-resolution borehole monitoring to the 4D surface data. The VSP data were acquired in well CFU 31-F1, which is the 3̃200 m deep CO2 injection well at the DAS site. A preinjection survey was recorded in late 2009 with injection beginning in December 2009, and a post injection survey was conducted in Nov 2010 following injection of about 250 kT of CO2. The sensor array for both surveys was a 50-level, 3-component, Sercel MaxiWave system with 15 m (49 ft) spacing between levels. The source for both surveys was an accelerated weight drop, with different source trucks used for the two surveys. Consistent time-lapse processing was applied to both data sets. Time-lapse processing generated difference corridor stacks to investigate CO2 induced reflection amplitude changes from each source point. Corridor stacks were used for amplitude analysis to maximize the signal-to-noise ratio (S/N) for each shot point. Spatial variation in reflectivity (used to ‘map’ the plume) was similar in magnitude to the corridor stacks but, due to relatively lower S/N, the results were less consistent and more sensitive to processing and therefore are not presented. We examined the overall time-lapse repeatability of the OVSP data using three methods, the NRMS and Predictability (Pred) measures of Kragh and Christie (2002) and the signal-to-distortion ratio (SDR) method of Cantillo (2011). Because time-lapse noise was comparable to the observed change, multiple methods were used to analyze data reliability. The reflections from the top and base reservoir were identified on the corridor stacks by correlation with a synthetic response generated from the well logs. A consistent change in the corridor stack amplitudes from pre- to post-CO2 injection was found for both the top and base reservoir reflections on all ten shot locations analyzed. In addition to the well-log synthetic response, a finite-difference elastic wave propagation model was built based on rock/fluid properties obtained from well logs, with CO2 induced changes guided by time-lapse crosswell seismic tomography (Ajo-Franklin, and others, 2013) acquired at the DAS site. Time-lapse seismic tomography indicated that two reservoir zones were affected by the flood. The modeling established that interpretation of the VSP trough and peak event amplitudes as reflectivity from the top and bottom of reservoir is appropriate even with possible tuning effects. Importantly, this top/base change gives confidence in an interpretation that these changes arise from within the reservoir, not from bounding lithology. The modeled time-lapse change and the observed field data change from 10 shotpoints are in agreement for both magnitude and polarity of amplitude change for top and base of reservoir. Therefore, we conclude the stored CO2 has been successfully detected and, furthermore, the observed seismic reflection change can be applied to Cranfield’s ...

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Page : 9 pages
File Size : 25,42 MB
Release : 2014
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A time-lapse Offset Vertical Seismic Profile (OVSP) data set was acquired as part of a subsurface monitoring program for geologic sequestration of CO2. The storage site at Cranfield, near Natchez, Mississippi, is part of a detailed area study (DAS) site for geologic carbon sequestration operated by the U.S. Dept. of Energy's Southeast Regional Carbon Sequestration Partnership (SECARB). The DAS site includes three boreholes, an injection well and two monitoring wells. The project team selected the DAS site to examine CO2 sequestration multiphase fluid flow and pressure at the interwell scale in a brine reservoir. The time-lapse (TL) OVSP was part of an integrated monitoring program that included well logs, crosswell seismic, electrical resistance tomography and 4D surface seismic. The goals of the OVSP were to detect the CO2 induced change in seismic response, give information about the spatial distribution of CO2 near the injection well and to help tie the high-resolution borehole monitoring to the 4D surface data. The VSP data were acquired in well CFU 31-F1, which is the ~3200 m deep CO2 injection well at the DAS site. A preinjection survey was recorded in late 2009 with injection beginning in December 2009, and a post injection survey was conducted in Nov 2010 following injection of about 250 kT of CO2. The sensor array for both surveys was a 50-level, 3-component, Sercel MaxiWave system with 15 m (49 ft) spacing between levels. The source for both surveys was an accelerated weight drop, with different source trucks used for the two surveys. Consistent time-lapse processing was applied to both data sets. Time-lapse processing generated difference corridor stacks to investigate CO2 induced reflection amplitude changes from each source point. Corridor stacks were used for amplitude analysis to maximize the signal-to-noise ratio (S/N) for each shot point. Spatial variation in reflectivity (used to 'map' the plume) was similar in magnitude to the corridor stacks but, due to relatively lower S/N, the results were less consistent and more sensitive to processing and therefore are not presented. We examined the overall time-lapse repeatability of the OVSP data using three methods, the NRMS and Predictability (Pred) measures of Kragh and Christie (2002) and the signal-to-distortion ratio (SDR) method of Cantillo (2011). Because time-lapse noise was comparable to the observed change, multiple methods were used to analyze data reliability. The reflections from the top and base reservoir were identified on the corridor stacks by correlation with a synthetic response generated from the well logs. A consistent change in the corridor stack amplitudes from pre- to post-CO2 injection was found for both the top and base reservoir reflections on all ten shot locations analyzed. In addition to the well-log synthetic response, a finite-difference elastic wave propagation model was built based on rock/fluid properties obtained from well logs, with CO2 induced changes guided by time-lapse crosswell seismic tomography (Ajo-Franklin, et al., 2013) acquired at the DAS site. Time-lapse seismic tomography indicated that two reservoir zones were affected by the flood. The modeling established that interpretation of the VSP trough and peak event amplitudes as reflectivity from the top and bottom of reservoir is appropriate even with possible tuning effects. Importantly, this top/base change gives confidence in an interpretation that these changes arise from within the reservoir, not from bounding lithology. The modeled time-lapse change and the observed field data change from 10 shotpoints are in agreement for both magnitude and polarity of amplitude change for top and base of reservoir. Therefore, we conclude the stored CO2 has been successfully detected and, furthermore, the observed seismic reflection change can be applied to Cranfield's ...

Author : Lianjie Huang
Publisher : John Wiley & Sons
Page : 468 pages
File Size : 47,27 MB
Release : 2022-04-05
Category : Science
ISBN : 1119156831

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Methods and techniques for monitoring subsurface carbon dioxide storage Storing carbon dioxide in underground geological formations is emerging as a promising technology to reduce carbon dioxide emissions in the atmosphere. A range of geophysical techniques can be deployed to remotely track carbon dioxide plumes and monitor changes in the subsurface, which is critical for ensuring for safe, long-term storage. Geophysical Monitoring for Geologic Carbon Storage provides a comprehensive review of different geophysical techniques currently in use and being developed, assessing their advantages and limitations. Volume highlights include: Geodetic and surface monitoring techniques Subsurface monitoring using seismic techniques Subsurface monitoring using non-seismic techniques Case studies of geophysical monitoring at different geologic carbon storage sites The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

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File Size : 16,67 MB
Release : 2009
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As a part of the effort of the Southwest Regional Partnership on Carbon Sequestration supported by U.S. Department of Energy and managed by the National Energy Technology Laboratory, two sets of time-lapse VSPs were acquired and processed in oil fields undergoing CO2 injection. One set of VSPs was acquired at the Aneth oil field in Utah, the other set at the Scurry Area Canyon Reef Operators Committee (SACROC) field in West Texas. One baseline and two repeat VSP surveys were conducted from 2007 to 2009 at the Aneth oil field in Utah for monitoring CO2 injection. The aim of the time-lapse VSP surveys is to study the combined enhanced oil recovery (EOR) and CO2 sequestration in collaboration with Resolute Natural Resources, Inc. VSP data were acquired using a cemented geophone string with 60 levels at depth from 805 m to 1704 m, and CO2 is injected into a horizontal well nearby within the reservoir at depth approximately from 1730 m to 1780 m. For each VSP survey, the data were acquired for one zero-offset source location and seven offset source locations (Figure 1). The baseline VSP survey was conducted before the CO2 injection. More than ten thousand tons of CO2 was injected between each of the two repeat VSP surveys. There are three horizontal injection wells, all originating from the same vertical well. One is drilled towards Southeast, directly towards the monitoring well (Figure 2), and the other two towards Northwest, directly away from the monitoring well. The injection is into the top portion of the Desert Creek formation, just beneath the Gothic shale, which acts as the reservoir seal. The initial baseline acquisition was done in October 2007; subsequent time-lapse acquisitions were conducted in July 2008, and January 2009. The acquisition geometry is shown in Figure 1. Shot point 1 is the zero-offset source location, Shot points 2 to 8 are the seven offset VSPs, arranged in a quarter circle on the Northwest side of the monitoring well. The horizontal injection well is shown in green. The black lines in Figure 1 show the approximate reflection coverage al reservoir depth from the respective offset source locations. VSP source location 5 is in a direct line with the injection. The 60 geophone sondes were cemented into the monitor well just before the baseline VSP acquisition and consisted of 96 geophone channels, with 18 three-component geophones (at the bottom of the string) and 42 single vertical component phones above. For this study, only the vertical geophone data were used.

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Page : 50 pages
File Size : 15,86 MB
Release : 2013
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This study was done to assess the repeatability and uncertainty of time-lapse VSP response to CO2 injection in the Frio formation near Houston Texas. A work flow was built to assess the effect of time-lapse injected CO2 into two Frio brine reservoir intervals, the 'C' sand (Frio1) and the 'Blue sand' (Frio2). The time-lapse seismic amplitude variations with sensor depth for both reservoirs Frio1 and Frio2 were computed by subtracting the seismic response of the base survey from each of the two monitor seismic surveys. Source site 1 has been considered as one of the best sites for evaluating the time-lapse response after injection. For site 1, the computed timelapse NRMS levels after processing had been compared to the estimated time-lapse NRMS level before processing for different control reflectors, and for brine aquifers Frio1, and Frio2 to quantify detectability of amplitude difference. As the main interest is to analyze the time-lapse amplitude variations, different scenarios have been considered. Three different survey scenarios were considered: the base survey which was performed before injection, monitor1 performed after the first injection operation, and monitor2 which was after the second injection. The first scenario was base-monitor1, the second was basemonitor2, and the third was monitor1-monitor2. We considered three 'control' reflections above the Frio to assist removal of overburden changes, and concluded that third control reflector (CR3) is the most favorable for the first scenario in terms of NRMS response, and first control reflector (CR1) is the most favorable for the second and third scenarios in terms of NRMS response. The NRMS parameter is shown to be a useful measure to assess the effect of processing on time-lapse data. The overall NRMS for the Frio VSP data set was found to be in the range of 30% to 80% following basic processing. This could be considered as an estimated baseline in assessing the utility of VSP for CO2 monitoring. This study shows that the CO2 injection in brine reservoir Frio1 (the 'C' sand unit) does induce a relative change in amplitude response, and for Frio2 (the 'Blue' sand unit) an amplitude change has been also detected, but in both cases the uncertainty, as measured by NRMS indicates the reservoir changes are, at best, only slightly above the noise level, and often below the noise level of the overall data set.

Author : Badr Waleed A Alrumaih
Publisher :
Page : pages
File Size : 19,52 MB
Release : 2019
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I present an approach for seismic monitoring from sparse time-lapse data, with a particular focus on leak detection from CO2 storage reservoirs. I use sparse data because it is (1) faster and (2) less expensive to acquire and to process, permitting for more frequent monitoring surveys to be carried out. This would allow for (1) early leak detection, which is what we ultimately aim for at a storage site, and (2) timely assessment of performance conformance. To account for data sparsity, I incorporate information on the underlying (injection) process (pressure and flow) into the geophysical model estimation. By process information, I mean how the geophysical model is possibly or potentially perturbed due to CO2 injection, as governed by the physics of the flow and the rock properties model. I do that by reformulating the geophysical minimization problem with Reduced-Order Basis (ROB) functions that are derived from simulated training images stochastically describing how the geophysical model is perturbed by the CO2 injection including leak possibilities, which I will refer to as ROB-inversion. Naturally, reducing the spatial sampling of the acquired data leads to reduced spatial resolution of the reconstructed subsurface model. This is the tradeoff for the increased calendar-time resolution, i.e., the shorter monitoring calendar-time interval. By reformulating the geophysical minimization problem with the process-derived reduced-order basis functions, I can improve the spatial resolution of the subsurface model—leading to approximate (or reduced-order) models. The accuracy of the reduced-order models depends on how representative the training image set is to the true model change. A key point in my implementation is the formulation of the problem in terms of the changes in model and data—not in terms of model and data. This (1) focuses the inversion on the model change, making it easy to apply restrictions and limitations on the model change during seismic inversion; the ROB-inversion essentially restricts the model change to be in terms of the (process-derived) Reduced-Order Basis functions. Furthermore, it (2) allows for the training images to be defined explicitly in terms of the time-lapse changes to the baseline model. The change is generally constrained—by the physics of the flow and the rock properties model, making a representative training image set to be reasonably attainable. An advantage of my approach over existing sparse time-lapse techniques is that it allows for fixed data acquisition configurations over calendar-time. Hence, the cost and turn-around time associated with redeployment of seismic data acquisition equipment can be minimized. In order to demonstrate my approach, I focus on borehole-based monitoring, namely, crosswell data acquisition geometry; nevertheless, it can be adapted to other geometries (surface-based or borehole-based) and other geophysical data (e.g., resistivity, electromagnetic, etc.). It can also be adapted for monitoring other processes, such as assessing the performance of Improved Oil Recovery (IOR). In this thesis, I demonstrate the practicability of my approach on synthetic and field traveltime crosswell datasets. I show, with synthetic and field data, its effectiveness for leak detection during CO2 injection.

Author : Shams Joon
Publisher :
Page : 0 pages
File Size : 47,20 MB
Release : 2022
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Geological carbon storage (GCS) is a climate change mitigation strategy that provides an innovative solution to offset the rising atmospheric CO2 concentrations. This process involves the injection of CO2 into underground geological formations where it is permanently trapped, thereby avoiding CO2 to be emitted into the atmosphere. The tax credit for CO2 sequestration (IRC Code: 45Q) has incentivized the feasibility of such operations and GCS is gaining substantial investment interest. The potential for CO2 to leak out and negatively impact the overlying environment is a primary concern for such operations and has motivated the development of risk-based monitoring, verification, and accounting (MVA) protocols around the world for Class II and Class VI wells. Fluid flow models are effective tools to simulate complex physical processes such as CO2 sequestration at a storage site. The accuracy of these models relies on multiple model parameters and state variables that are calibrated to reproduce the changing reservoir state. Geophysical monitoring data from multiple sources are used to further calibrate reservoir simulations and improve model accuracy. However, both the reservoir model and geophysical measurements produce uncertain predictions due to the underlying process and measurement errors. Monitoring tools can be evaluated based on their sensitivity, spatiotemporal coverage, cost, and regulatory requirements. Wellbore sensors, such as pressure gauges, provide high temporal sampling of the subsurface but are spatially limited to around the wellbore. In contrast, surface seismics can survey large volumes of the reservoir with a coarse spatial resolution and are costly which limits how frequently they can be conducted. Furthermore, using these types of geophysical monitoring tools to estimate changes in petrophysical properties is always subject to uncertainty due to inevitable ambiguities incurred during data acquisition, processing, and interpretation. Combining multiple sources of measurements can help reduce prediction uncertainty; however, quantifying the improvement afforded by such composite systems can be a challenging task when the true reservoir characteristics are unknown. Quantifying the reduction in prediction error from different monitoring tools and combinations of monitoring tools can also be useful to evaluate the efficacy of a proposed monitoring design. From a monitoring design perspective, this research validates the applicability of combining seismic attributes derived from full-waveform inversion of continuous active-source seismic monitoring (CASSM) data with pressure-based monitoring measurements to improve model state predictions. The improvement afforded by combining these two different types of measurements is quantified by computing the reduction in prediction error in an ensemble-based data assimilation environment. The first goal of this research is to develop and test out an ensemble-based data assimilation framework that takes advantage of rock physics models and combines numerical simulations with geophysical observations to predict subsurface changes at GCS sites. This proposed joint seismic-pressure-petrophysical data assimilation framework uses continuous geophysical measurements, in the form of seismic velocity (Vp) and seismic attenuation quality factor (Qp) along with wellbore pressure monitoring data (Pwf), to predict changes in the reservoir model state which is represented by CO2 saturation and reservoir pressure distributions. One of the challenges of using seismic data is the non-unique relationship between CO2 fluid properties and seismic attributes which introduces ambiguity (multiple solutions) during inversion. Rock physics models can be used to forward model seismic attributes but due to the highly non-linear nature of these models and the multidimensionality of reservoir rock and fluid properties, standard linear models are rendered unusable for inversion purposes. Combining different types of measurements (seismic with pressure) helps further constrain this non-uniqueness and improves the forward-modeled estimates. These multi-sensor measurements are assimilated using an ensemble Kalman filter (EnKF) which propagates the model state and uncertainty forward using an ensemble of reservoir realization and relies on ensemble-based sample statistics of the model state and measurement error to calibrate estimates when new measurements are made available. One of the novelties of this workflow is that the forward operator of the EnKF is replaced with rock physics models (RPMs). The choice of rock physics model depends on the geological context, the rock and fluid properties, operational parameters of the seismic survey, and available seismic attributes. I use one particular RPM i.e., White's patchy gas saturation model that we use for demonstration purposes, but one could use this general framework to employ any one of a variety of RPMs. I conduct a series of observation system simulation experiments (OSSEs) to demonstrate the effectiveness of this joint data assimilation framework by evaluating different monitoring tools and combination of monitoring tools on three different models. The OSSEs are first conducted on a lab-scale "sandbox" model before being tested on field-scale reservoir models like the Frio II brine pilot, near Houston, Texas and the Cranfield Site in Mississippi. In general, including seismic attributes improves the prediction estimate of CO2 saturation while Pwf measurements improve pressure prediction results by calibrating the well constraints and improving model state forecasts. Jointly assimilating both seismic and pressure data produces the greatest reduction in prediction error and the high temporal resolution afforded by continuous seismic measurements allows for shorter assimilation windows. Reducing the assimilation frequency increases the prediction error which is observed when CO2 injection is halted and the post-injection assimilation time window is increased. This improvement afforded by jointly assimilating multi-sensor observations is consistently observed in all three synthetic case studies even when different data assimilation parameters are varied such as type, ensemble size, assimilation frequency etc. After successfully implementing the multi-sensor, rock physics-based data assimilation framework in an OSSE environment, I integrate the framework with full-waveform inversion (FWI) results from the CASSM dataset at Frio II. In this work, the CASSM-derived FWI seismic attributes and wellbore pressure monitoring data are jointly assimilated to predict CO2 plume movement and reservoir pressure changes over a 5-day injection period. A comprehensive comparison of using a multi-sensor approach as compared to just wellbore pressure sensors is carried out to conclude that the error reduction afforded by using multiple sensors is valuable both from a perspective of risk as well as cost. Lastly, the multi-sensor, rock physics-based data assimilation framework is reconfigured for additional operational applications at GCS sites like observation targeting. In particular, this modified workflow takes advantage of ensemble-based sensitivity analysis to evaluate how changing the placement location of monitoring wells influences the prediction uncertainty of model state variables. Furthermore, by evaluating the efficacy of pre-existing and/or limited monitoring tools and designs, one can identify regions of the reservoir with highest uncertainty and subsequently find optimal locations for drilling new monitoring wells. A series of OSSEs of the Frio II reservoir model are used to demonstrate the applicability of this observation targeting approach.

Author : Thomas L. Davis
Publisher : Cambridge University Press
Page : 391 pages
File Size : 18,73 MB
Release : 2019-05-09
Category : Business & Economics
ISBN : 1107137497

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An overview of the geophysical techniques and analysis methods for monitoring subsurface carbon dioxide storage for researchers and industry practitioners.