Permeability and injectivity improvement in CO2 enhanced coalbed methane recovery : thermal stimulation of the near wellbore region

Non peer reviewedPublisher PD

Two Phase Relative Permeability of Gas and Water in Coal for Enhanced Coalbed Methane Recovery and CO2 Storage

Non peer reviewedPublisher PD

Multi-period Least Cost Optimisation Model of an Integrated Carbon Dioxide Capture Transportation and Storage Infrastructure in the UK

AbstractThe commercial deployment of CO2 capture and storage (CCS) technology requires whole system optimisation of the CO2 supply, transport and storage chain under evolving targets or constraints. Most of the earlier attempts to model CCS networks were deterministic steady state models. The very few multi-period spatially explicit CCS models are unable to simultaneously make investment decisions for the three components of the chain for an overall cost optimal solution or they only demonstrate the evolution of the transport network. This work presents a multi-period spatially explicit least cost optimisation model of an integrated CO2 capture, transportation and storage infrastructure. The model is showcased through a case study focusing on the future UK CCS infrastructure. The solution demonstrates the investment requirement and operational strategy for all components of the chain at each phase and, hence, shows how the system evolves through four time periods up to year 2050. The non- intuitive results of the multi-period model confirm that such a tool is essential for large scale CCS deployment

Implementation of horizontal well CBM/ECBM technology and the assessment of effective CO2 storage capacity in a Scottish coalfield

Acknowledgements The authors wish to thank Composite Energy Ltd., the BG Group, Scottish Power and the Royal Bank of Scotland for their funding and contributions towards the research reported in this paper.Non peer reviewedPublisher PD

Calculation of pressure- and migration-constrained dynamic CO2 storage capacity of the North Sea Forties and Nelson dome structures

This paper presents a numerical simulation study of CO2 injection into the Forties and Nelson dome structures in the North Sea. The study assumes that these structures are fully depleted of their remaining hydrocarbon and brine has replaced their pore space, and therefore the structures can be treated as saline aquifers. Under this assumption, the objective is to calculate the dynamic CO2 storage capacity of the Forties and Nelson structures and design an injection scenario to enhance storage utilisation. In doing so, first, a detailed geological model of the dome structures and their surrounding aquifer is developed to represent the lithological facies associations and attribute them with petrophysical properties. The geological model is calibrated in terms of the surrounding aquifer support using the hydrocarbon production data. The dynamic storage capacity is subsequently estimated by numerical simulation of the two-phase (brine and CO2) process. Key performance indicators (KPIs), such as the pressure build-up and regional mass fraction of CO2, are used to constrain the injection scenarios that consequently result in the best capacity utilisation of the storage structures. In our model of fully brine saturated dome structures, based on specific constraints, namely <0.1% of the total gaseous CO2 outside the dome into an upper pressure unit and 66% of the initial hydrostatic pressure as the allowable increase in the bottom-hole pressure, we obtained a dynamic capacity of 121 million tonnes for the Forties structure and 24 million tonnes for the Nelson structure. These values are subject to change when a three phase model of residual oil, gas and water is considered in simulations

The Effect of Market and Leasing Conditions on the Techno-economic Performance of Complex CO2 transport and storage value chains

AbstractThe complex interplay of capital and operating costs that results from different CO2 transport and storage network configurations, and the market conditions in which they develop is investigated using the life cycle CO2 storage cost model and the multi-period CCS network optimisation model developed by Imperial College. These tools integrate seamlessly the geological characteristics, engineering aspects and the economics of complex CCS chains. The paper demonstrates that these models capture effectively and accurately the effects of market and leasing conditions on the techno-economic performance of complex CCS value chains. The results reveal that saline aquifers and depleted oil and gas fields may differ significantly in terms of cost performance. It is also shown that it is important to evaluate the technical and economic performance of the CCS value chain as a whole, rather than in individual components in order to ensure the financial viability of CCS projects

Evolutionary optimisation for CO2 storage design using upscaled models: application on a proximal area of the Forties Fan System in the UK Central North Sea

Optimisation of injection rates is an important design consideration for meeting operational objectives and ensuring long term geological storage of CO2 in saline aquifers. The optimal design should also take into account the uncertainties associated with the subsurface (e.g., petrophysical attribution and structural relationships). Detailed geological models along with different realisations for handling uncertainties increase the computational overheads, making the optimisation problem intractable. To circumvent this problem, upscaled models can be used to speed up the identification of optimal solutions. Nevertheless, a grid resolution, which does not compromise the accuracy of the optimisation in an upscaled model, must be carefully determined. The methodology described in this paper aims to address this requirement. In this study, a 3D geological model, comprising the main oil reservoirs of the Forties and Nelson hydrocarbon fields and the adjacent saline aquifer, was built to examine the use of coarse grid resolutions to design an optimal CO2 storage solution for this area within the UK Central North Sea. Simulation results for single objective optimisation show that an upscaled grid resolution can be identified which is a trade-off between accuracy and computational time. The outlined methodology is generic in nature and can be ported to other similar optimisation problems for CO2 storage

CO2 storage well rate optimisation in the Forties sandstone of the Forties and Nelson reservoirs using evolutionary algorithms and upscaled geological models

Optimisation is particularly important in the case of CO2 storage in saline aquifers, where there are various operational objectives to be achieved. The storage operation design process must also take various uncertainties into account, which result in adding computational overheads to the optimisation calculations. To circumvent this problem upscaled models with which computations are orders of magnitude less time-consuming can be used. Nevertheless, a grid resolution, which does not compromise the accuracy, reliability and robustness of the optimisation in an upscaled model must be carefully determined. In this study, a 3D geological model based on the Forties and Nelson hydrocarbon fields and the adjacent saline aquifer, is built to examine the use of coarse grid resolutions to design an optimal CO2 storage solution. The optimisation problem is to find optimal allocation of total CO2 injection rate between existing wells. A simulation template of an area encompassing proximal-type reservoirs of the Forties-Montrose High is considered. The detailed geological model construction leads to computationally intensive simulations for CO2 storage design, so that upscaling is rendered unavoidable. Therefore, an optimal grid resolution that successfully trades accuracy against computational run-time is sought after through a thorough analysis of the optimisation results for different resolution grids. The analysis is based on a back-substitution of the optimisation solutions obtained from coarse-scale models into the fine-scale model, and comparison between these back-substitution models and direct use of fine-scale model to conduct optimisation

Development of key performance indicators for CO2 storage operability and efficiency assessment: application to the Southern North Sea Rotliegend Group

This paper outlines the development of a methodology which can be used to produce key performance indicators for operability and efficiency of a CO2 storage site. The methodology is based on the premise that individual geological formations and their characteristics can be assessed on the basis of their depositional and tectonic setting and more recent reservoir/site history using hydrocarbon exploration and development data. The methodology is illustrated for a candidate storage reservoir in the Rotliegend Leman Sandstone Formation of the UK Southern North Sea

Development of support vector machine learning algorithm for real time update of resource estimation and grade classification

This paper presents the development and implementation of a theoretical mathematical-statistical framework for sequential updating of the grade control model, based on a support vector machine learning algorithm. Utilising the Zambujal orebody within the Neves-Corvo Cu deposit in Portugal, parameters that can be measured in real time, used in visualisation, modelled for resource estimation, and used for process control visualisation and optimisation are considered. The methodology broadly comprises of three steps. Firstly, the provided dataset is used to develop a virtual asset model (VAM) representing the true 3D grade distribution in order to simulate the mining method. Then ore quality parameters are established simulating real time monitoring sensor installation at: (a) stope development and rock face monitoring (face imaging and drillholes); and (b) transport monitoring (muck pile, LHD/scooptram). Next, the acquired data was assimilated into the models as part of the sequential model update. Two different mining methods and the monitoring information that can be acquired during the ore extraction are analysed: (a) drift and fill mining and (b) bench and fill mining, which are widely implemented at the Neves-Corvo mine. Selected study zones were chosen such as to contrast mining through the high/low grade zones with different degrees of heterogeneity, which demonstrate the performance of resource estimation and classification models developed in heterogeneous mining stopes. The grade accuracy and error in the resource model, and high/low grade ore classification accuracy and error are evaluated as performance metrics for the proposed methods. In drift and fill mining, drillhole and face sampling data collection was simulated in a real-time manner and fed into the support vector machine (SVM) regressor to update the resource estimation model in both a high grade and low grade drift scenarios. In each scenario, six drift and fill mining steps were simulated sequentially and the posterior resource models, after integrating real time mining data, have shown significant improvement of bias correction in both updating planned resources and reconciling extracted ore. In bench and fill mining, grade classification based on random sampling data from muck pile was demonstrated, considering scoop by scoop derived monitoring data. Three different classifiers (mean, median, and Bayesian) were tested and shown very good performance. In the case study presented here, a sequence of 15 blasting steps was simulated with each step requiring 112 scooping operations to transport the blasted ore. Using the real time monitored information, it was shown that at each blasting step over 85% of the scoops can be labelled correctly using the proposed methods and with an accuracy of over 95%