Robust CO2 Plume Imaging Using Joint Tomographic Inversion Of Distributed Pressure And Temperature Measurements

2021 ◽  
Author(s):  
Changqing Yao ◽  
Hongquan Chen ◽  
Akhil Datta-Gupta ◽  
Sanjay Mawalkar ◽  
Srikanta Mishra ◽  
...  
Keyword(s):  
Carbon Sequestration ◽  
History Matching ◽  
Temperature Measurements ◽  
Injection Well ◽  
Co2 Injection ◽  
Pressure Data ◽  
Distributed Temperature Sensing ◽  
Data Set ◽  
Monitoring Well ◽  
Co2 Plume

Abstract Geologic CO2 sequestration and CO2 enhanced oil recovery (EOR) have received significant attention from the scientific community as a response to climate change from greenhouse gases. Safe and efficient management of a CO2 injection site requires spatio-temporal tracking of the CO2 plume in the reservoir during geologic sequestration. The goal of this paper is to develop robust modeling and monitoring technologies for imaging and visualization of the CO2 plume using routine pressure/temperature measurements. The streamline-based technology has proven to be effective and efficient for reconciling geologic models to various types of reservoir dynamic response. In this paper, we first extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The power and utility of our approach are demonstrated using both synthetic and field applications. We first validate the streamline-based DTS data inversion using a synthetic example. Next, the hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warmback analysis of the temperature data. The novelty of this work is the streamline-based history matching method for the DTS data and its field application to the Department of Engergy regional carbon sequestration project in Michigan.

scholarly journals Numerical Simulation and Optimization of CO2 Sequestration in Saline Aquifers

2012 ◽  
Author(s):  
Zheming Zhang ◽  
Ramesh Agarwal
Keyword(s):  
Numerical Simulation ◽  
Co2 Sequestration ◽  
Storage Capacity ◽  
Co2 Storage ◽  
Injection Well ◽  
Co2 Injection ◽  
Great Promise ◽  
Saline Aquifer ◽  
Simulation And Optimization ◽  
Co2 Plume

With recent concerns on CO2 emissions from coal fired electricity generation plants; there has been major emphasis on the development of safe and economical Carbon Dioxide Capture and Sequestration (CCS) technology worldwide. Saline reservoirs are attractive geological sites for CO2 sequestration because of their huge capacity for sequestration. Over the last decade, numerical simulation codes have been developed in U.S, Europe and Japan to determine a priori the CO2 storage capacity of a saline aquifer and provide risk assessment with reasonable confidence before the actual deployment of CO2 sequestration can proceed with enormous investment. In U.S, TOUGH2 numerical simulator has been widely used for this purpose. However at present it does not have the capability to determine optimal parameters such as injection rate, injection pressure, injection depth for vertical and horizontal wells etc. for optimization of the CO2 storage capacity and for minimizing the leakage potential by confining the plume migration. This paper describes the development of a “Genetic Algorithm (GA)” based optimizer for TOUGH2 that can be used by the industry with good confidence to optimize the CO2 storage capacity in a saline aquifer of interest. This new code including the TOUGH2 and the GA optimizer is designated as “GATOUGH2”. It has been validated by conducting simulations of three widely used benchmark problems by the CCS researchers worldwide: (a) Study of CO2 plume evolution and leakage through an abandoned well, (b) Study of enhanced CH4 recovery in combination with CO2 storage in depleted gas reservoirs, and (c) Study of CO2 injection into a heterogeneous geological formation. Our results of these simulations are in excellent agreement with those of other researchers obtained with different codes. The validated code has been employed to optimize the proposed water-alternating-gas (WAG) injection scheme for (a) a vertical CO2 injection well and (b) a horizontal CO2 injection well, for optimizing the CO2 sequestration capacity of an aquifer. These optimized calculations are compared with the brute force nearly optimized results obtained by performing a large number of calculations. These comparisons demonstrate the significant efficiency and accuracy of GATOUGH2 as an optimizer for TOUGH2. This capability holds a great promise in studying a host of other problems in CO2 sequestration such as how to optimally accelerate the capillary trapping, accelerate the dissolution of CO2 in water or brine, and immobilize the CO2 plume.

scholarly journals Snøhvit CO2 Storage Project: Assessment of CO2 Injection Performance Through History Matching of the Injection Well Pressure Over a 32-months Period

2013 ◽  
Vol 37 ◽  
pp. 3267-3274 ◽  
Author(s):  
Ji-Quan Shi ◽  
Claire Imrie ◽  
Caglar Sinayuc ◽  
Sevket Durucan ◽  
Anna Korre ◽  
...  
Keyword(s):  
History Matching ◽  
Co2 Storage ◽  
Injection Well ◽  
Co2 Injection ◽  
Project Assessment

scholarly journals Development of an Electrical Resistivity Tomography Monitoring Concept for the Svelvik CO<sub>2</sub> Field Lab, Norway

2020 ◽  
Vol 54 ◽  
pp. 41-53
Author(s):  
Tobias Raab ◽  
Wolfgang Weinzierl ◽  
Bernd Wiese ◽  
Dennis Rippe ◽  
Cornelia Schmidt-Hattenberger
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Co2 Storage ◽  
Pressure Effects ◽  
Co2 Injection ◽  
Target Area ◽  
Electrode Arrays ◽  
Resistivity Tomography ◽  
Monitoring Well ◽  
Co2 Plume

Abstract. Within the ERA-NET co-funded ACT project Pre-ACT (Pressure control and conformance management for safe and efficient CO2 storage – Accelerating CCS Technologies), a monitoring concept was established to distinguish between CO2 induced saturation and pore pressure effects. As part of this monitoring concept, geoelectrical cross-hole surveys have been designed and conducted at the Svelvik CO2 Field Lab, located on the Svelvik ridge at the outlet of the Drammensfjord in Norway. The Svelvik CO2 Field Lab has been established in summer 2019, and comprises four newly drilled, 100 m deep monitoring wells, surrounding an existing well used for water and CO2 injection. Each monitoring well was equipped with modern sensing systems including five types of fiber-optic cables, conventional- and capillary pressure monitoring systems, as well as electrode arrays for Electrical Resistivity Tomography (ERT) surveys. With a total of 64 electrodes (16 each per monitoring well), a large number of measurement configurations for the ERT imaging is possible, requiring the performance of the tomography to be investigated beforehand by numerical studies. We combine the free and open-source geophysical modeling library pyGIMLi with Eclipse reservoir modeling to simulate the expected behavior of all cross-well electrode configurations during the CO2 injection experiment. Simulated CO2 saturations are converted to changes in electrical resistivity using Archie's Law. Using a finely meshed resistivity model, we simulate the response of all possible measurement configurations, where always two electrodes are located in two corresponding wells. We select suitable sets of configurations based on different criteria, i.e. the ratio between the measured change in apparent resistivity in relation to the geometric factor and the maximum sensitivity in the target area. The individually selected measurement configurations are tested by inverting the synthetic ERT data on a second coarser mesh. The pre-experimental, numerical results show adequate resolution of the CO2 plume. Since less CO2 was injected during the field experiment than originally modeled, we perform post-experimental tests of the selected configurations for their potential to image the CO2 plume using revised reservoir models and injection volumes. These tests show that detecting the small amount of injected CO2 will likely not be feasible.

scholarly journals Managing Uncertainty in Geological CO2 Storage Using Bayesian Evidential Learning

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1557
Author(s):  
Amine Tadjer ◽  
Reidar B. Bratvold
Keyword(s):  
Uncertainty Quantification ◽  
History Matching ◽  
Co2 Storage ◽  
Carbon Capture And Storage ◽  
Monitoring Program ◽  
Co2 Injection ◽  
Drinking Water Contamination ◽  
Model Inversion ◽  
Geological Co2 Storage ◽  
And Storage

Carbon capture and storage (CCS) has been increasingly looking like a promising strategy to reduce CO2 emissions and meet the Paris agreement’s climate target. To ensure that CCS is safe and successful, an efficient monitoring program that will prevent storage reservoir leakage and drinking water contamination in groundwater aquifers must be implemented. However, geologic CO2 sequestration (GCS) sites are not completely certain about the geological properties, which makes it difficult to predict the behavior of the injected gases, CO2 brine leakage rates through wellbores, and CO2 plume migration. Significant effort is required to observe how CO2 behaves in reservoirs. A key question is: Will the CO2 injection and storage behave as expected, and can we anticipate leakages? History matching of reservoir models can mitigate uncertainty towards a predictive strategy. It could prove challenging to develop a set of history matching models that preserve geological realism. A new Bayesian evidential learning (BEL) protocol for uncertainty quantification was released through literature, as an alternative to the model-space inversion in the history-matching approach. Consequently, an ensemble of previous geological models was developed using a prior distribution’s Monte Carlo simulation, followed by direct forecasting (DF) for joint uncertainty quantification. The goal of this work is to use prior models to identify a statistical relationship between data prediction, ensemble models, and data variables, without any explicit model inversion. The paper also introduces a new DF implementation using an ensemble smoother and shows that the new implementation can make the computation more robust than the standard method. The Utsira saline aquifer west of Norway is used to exemplify BEL’s ability to predict the CO2 mass and leakages and improve decision support regarding CO2 storage projects.

Artificial Intelligence Coreflooding Simulator for Special Core Data Analysis

2021 ◽  
pp. 1-29
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Artificial Intelligence ◽  
Steady State ◽  
History Matching ◽  
Water Saturation ◽  
Flow Behavior ◽  
Model Performance ◽  
Analysis Data ◽  
Coefficient Of Determination ◽  
Water Drainage ◽  
Data Set

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

scholarly journals Capillary processes increase salt precipitation during CO2 injection in saline formations

2018 ◽  
Vol 852 ◽  
pp. 398-421
Author(s):  
Helena L. Kelly ◽  
Simon A. Mathias
Keyword(s):  
High Salinity ◽  
Volume Fraction ◽  
Injection Rate ◽  
Injection Well ◽  
Co2 Injection ◽  
Salt Precipitation ◽  
Injection Wells ◽  
Tenfold Increase ◽  
Entry Pressure ◽  
Injection Rates

An important attraction of saline formations for CO2 storage is that their high salinity renders their associated brine unlikely to be identified as a potential water resource in the future. However, high salinity can lead to dissolved salt precipitating around injection wells, resulting in loss of injectivity and well deterioration. Earlier numerical simulations have revealed that salt precipitation becomes more problematic at lower injection rates. This article presents a new similarity solution, which is used to study the relationship between capillary pressure and salt precipitation around CO2 injection wells in saline formations. Mathematical analysis reveals that the process is strongly controlled by a dimensionless capillary number, which represents the ratio of the CO2 injection rate to the product of the CO2 mobility and air-entry pressure of the porous medium. Low injection rates lead to low capillary numbers, which in turn are found to lead to large volume fractions of precipitated salt around the injection well. For one example studied, reducing the CO2 injection rate by 94 % led to a tenfold increase in the volume fraction of precipitated salt around the injection well.

3D DAS-VSP Illumination Modeling for CO2 Plume Migration Monitoring in Offshore Sarawak, Malaysia

2021 ◽  
Author(s):  
Pankaj Kumar Tiwari ◽  
Zoann Low ◽  
Parimal Arjun Patil ◽  
Debasis Priyadarshan Das ◽  
Prasanna Chidambaram ◽  
...  
Keyword(s):  
Dynamic Simulation ◽  
Seismic Velocity ◽  
Time Lapse ◽  
Carbonate Reservoir ◽  
Fiber Optic Sensor ◽  
Co2 Injection ◽  
Storage Site ◽  
Plume Migration ◽  
Co2 Plume ◽  
4D Seismic

Abstract Monitoring of CO2 plume migration in a depleted carbonate reservoir is challenging and demand comprehensive and trailblazing monitoring technologies. 4D time-lapse seismic exhibits the migration of CO2 plume within geological storage but in the area affected by gas chimney due to poor signal-to-noise ratio (SNR), uncertainty in identifying and interpretation of CO2 plume gets exaggerated. High resolution 3D vertical seismic profile (VSP) survey using distributed acoustic sensor (DAS) technology fulfil the objective of obtaining the detailed subsurface image which include CO2 plume migration, reservoir architecture, sub-seismic faults and fracture networks as well as the caprock. Integration of quantitative geophysics and dynamic simulation with illumination modelling dignify the capabilities of 3D DAS-VSP for CO2 plume migration monitoring. The storage site has been studied in detailed and an integrated coupled dynamic simulation were performed and results were integrated with seismic forward modeling to demonstrate the CO2 plume migration with in reservoir and its impact on seismic amplitude. 3D VSP illumination modelling was carried out by integrating reservoir and overburden interpretations, acoustic logs and seismic velocity to illustrate the subsurface coverage area at top of reservoir. Several acquisition survey geometries were simulated based on different source carpet size for effective surface source contribution for subsurface illumination and results were analyzed to design the 3D VSP survey for early CO2 plume migration monitoring. The illumination simulation was integrated with dynamic simulation for fullfield CO2 plume migration monitoring with 3D DAS-VSP by incorporating Pseudo wells illumination analysis. Results of integrated coupled dynamic simulation and 4D seismic feasibility were analyzed for selection of best well location to deploy the multi fiber optic sensor system (M-FOSS) technology. Amplitude response of synthetic AVO (amplitude vs offsets) gathers at the top of carbonate reservoir were analyzed for near, mid and far angle stacks with respect to pre-production as well as pre-injection reservoir conditions. Observed promising results of distinguishable 25-30% of CO2 saturation in depleted reservoir from 4D time-lapse seismic envisage the application of 3D DAS-VSP acquisition. The source patch analysis of 3D VSP illumination modelling results indicate that a source carpet of 6km×6km would be cos-effectively sufficient to produce a maximum of approximately 2km in diameter subsurface illumination at the top of the reservoir. The Pseudo wells illumination analysis results show that current planned injection wells would probably able to monitor early CO2 injection but for the fullfield monitoring additional monitoring wells or a hybrid survey of VSP and surface seismic would be required. The integrated modeling approach ensures that 4D Seismic in subsurface CO2 plume monitoring is robust. Monitoring pressure build-ups from 3D DAS-VSP will reduce the associated risks.

History Matching Using 4D Seismic and Pressure Data on the Norne Field

2005 ◽  
Author(s):  
M. Lygren ◽  
O. Husby ◽  
B. Osdal ◽  
Y. El Ouair ◽  
M. Springer
Keyword(s):  
History Matching ◽  
Pressure Data ◽  
4D Seismic
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