Imaging of a fluid injection process using geophysical data - A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts

Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability

Assessing The Probability Of Fluid Migration Caused By Hydraulic Fracturing; And Investigating Flow And Transport In Porous Media Using Mri

Hydraulic fracturing is used to extract oil and natural gas from low permeability formations. The potential of fluids migrating from depth through adjacent wellbores and through the production wellbore was investigated using statistical modeling and predic-tive classifiers. The probability of a hydraulic fracturing well becoming hydraulically connected to an adjacent well in the Marcellus shale of New York was determined to be between 0.00% and 3.45% at the time of the study. This means that the chance of an in-duced fracture from hydraulic fracturing intersecting an existing well is highly dependent on the area of increased permeability caused by fracturing. The chance of intersecting an existing well does not mean that fluid will flow upwards; for upward migration to occur, a pathway must exist and a pressure gradient is required to drive flow, with the exception of gas flow caused by buoyancy. Predictive classifiers were employed on a dataset of wells in Alberta Canada to identify well characteristics most associated to fluid migration along the production well. The models, specifically a random forest, were able to identify pathways better than random guessing with 78% of wells in the data set identified cor-rectly. Magnetic resonance imaging (MRI) was used to visualize and quantify contami-nant transport in a soil column using a full body scanner. T1 quantification was used to determine the concentration of a contaminant surrogate in the form of Magnevist, an MRI contrast agent. Imaging showed a strong impact from density driven convection when the density difference between the two fluids was small (0.3%). MRI also identified a buildup of contrast agent concentration at the interface between a low permeability ground silica and higher permeability AFS 50-70 testing sand when density driven con-vection was eliminated

Developing A Machine Learning Based Approach For Fractured Zone Detection By Using Petrophysical Logs

Oil reservoirs are divided into three categories: carbonate (fractured), sandstone and unconventional reservoirs. Identification and modeling of fractures in fractured reservoirs are so important due to geomechanical issues, fluid flood simulation and enhanced oil recovery.Image and petrophysical logs are individual tools, run inside oil wells, to achieve physical characteristics of reservoirs, e.g. geological rock types, porosity, and permeability. Fractures could be distinguished using image logs because of their higher resolution. Image logs are an expensive and newly developed tool, so they have run in limited wells, whereas petrophysical logs are usually run inside the wells. Lack of image logs makes huge difficulties in fracture detection, as well as fracture studies. In the last decade, a few studies were done to distinguish fractured zones in oil wells, by applying data mining methods over petrophysical logs. The goal of this study was also discrimination of fractured/non-fractured zones by using machine learning techniques and petrophysical logs. To do that, interpretation of image logs was utilized to label reservoir depth of studied wells as 0 (non-fractured zone) and 1 (fractured zone). We developed four classifiers (Deep Learning, Support Vector Machine, Decision Tree, and Random Forest) and applied them to petrophysics logs to discriminate fractured/non-fractured zones. Ordered Weighted Averaging was the data fusion method that we utilized to integrate outputs of classifiers in order to achieve unique and more reliable results. Overall, the frequency of non-fractured zones is about two times of fractured zones. This leads to an imbalanced condition between two classes. Therefore, the aforementioned procedure relied on the balance/imbalance data to investigate the influence of creating a balanced situation between classes. Results showed that Random Forest and Support Vector Machines are better classifiers with above 95 percent accuracy in discrimination of fractured/non-fractured zones. Meanwhile, making a balanced situation in the wells by a higher imbalance index helps to distinguish either non-fractured or fractured zones. Through imbalance data, non-fractured zones (dominant class) could be perfectly distinguished, while a significant percentage of fractured zones were also labeled as non-fractured ones

Developing tools for determination of parameters involved in CO₂ based EOR methods

To mitigate the effects of climate change, CO₂ reduction strategies are suggested to lower anthropogenic emissions of greenhouse gasses owing to the use of fossil fuels. Consequently, the application of CO₂ based enhanced oil recovery methods (EORs) through petroleum reservoirs turn into the hot topic among the oil and gas researchers. This thesis includes two sections. In the first section, we developed deterministic tools for determination of three parameters which are important in CO₂ injection performance including minimum miscible pressure (MMP), equilibrium ratio (Kᵢ), and a swelling factor of oil in the presence of CO₂. For this purposes, we employed two inverse based methods including gene expression programming (GEP), and least square support vector machine (LSSVM). In the second part, we developed an easy-to-use, cheap, and robust data-driven based proxy model to determine the performance of CO₂ based EOR methods. In this section, we have to determine the input parameters and perform sensitivity analysis on them. Next step is designing the simulation runs and determining the performance of CO₂ injection in terms of technical viewpoint (recovery factor, RF). Finally, using the outputs gained from reservoir simulators and applying LSSVM method, we are going to develop the data-driven based proxy model. The proxy model can be considered as an alternative model to determine the efficiency of CO₂ based EOR methods in oil reservoir when the required experimental data are not available or accessible

Numerical study of underground CO2 storage and the utilization in depleted gas reservoirs

The emission of atmospheric CO2 is the main contributor to global warming and climate change. Carbon capture and storage (CCS) is considered as the most promising technology for slowing down the atmospheric CO2 emissions. Meanwhile, CCS is beneficial for the circulation carbon economy. However, CCS has not been implemented on large scale because of the related risks and the lack of economic incentives. This thesis attempts to focus on these two problems and provide some strategies to address them. Regarding the risks associated with CCS, a parametric uncertainty analysis for CO2 storage was conducted and the general role of different geomechanical and hydrogeological parameters in response to CO2 injection was determined. Regarding the financial incentives of CCS operation, this thesis attempts to increase the cost-effectiveness of CCS through co-injecting CO2 with impurities associated with enhanced gas recovery (CSEGR) and using CO2 as cushion gas in the underground gas storage reservoir (UGSR). In order to understand the thermal-hydrological-mechanical (THM) process of CO2 storage, the THM coupled simulator TOUGH2MP (TMVOC)-FLAC3D was developed. By using the developed TOUGH2MP (TMVOC)-FLAC3D simulator, numerical simulation for hundreds of sampled data was performed for results generated by the Quasi-Monte Carlo method. Based on the simulation results, the general role of different geomechanical and hydrogeological parameters was determined in response to CO2 injection using distance correlation. In addition, a risk factor was defined to characterize the risks of the caprock due to CO2 injection. The results showed that the reservoir permeability and the injection rate are the two most important factors in determining the pressure change. Moreover, the reservoir Young’s modulus plays the most vital role in formation deformation including vertical displacement. The pressure change exhibits a much closer correlation with the risk factor in comparison to the formation deformation, indicating the importance of pressure change in the integrity assessment of the caprock. By using the machine learning approach in support vector regression (SVR), the SVR surrogate model was well-trained based on the data regarding simulated results, and its reliability was verified using the test data. Thereafter, the formation response including the pressure change as well as formation deformation, can be predicted using the trained SVR surrogate model within a very short time. The methods and working scheme applied in this work can be used to guide time and effort spent mitigating the uncertainty in these parameters to acquire trustworthy model forecasts and risk assessments in CCS projects. Attempting to decrease the cost of CCS operation, CO2 injection with impurity gas, i.e., N2 and O2, into a depleted gas reservoir was investigated. The impacts of the key parameters on the performance of CO2 storage and CSEGR were analyzed in detail. The results showed that the effect of impurities on CO2 storage capacity is dependent on the reservoir pressure and temperature conditions, and the concentration of impurities. The depleted gas reservoir with a relatively low temperature and low irreducible water saturation is favorable to the CO2 storage capacity. A low primary gas recovery for the depleted gas reservoir is in favor of CSEGR, while it is suitable for dedicated CO2 storage when the primary gas recovery is high. In addition, it is suggested to produce the CH4 as possible before the operation of CO2 storage and CSEGR. The chromatographic partitioning phenomenon may occur when N2 and O2 were co-injected with CO2 into depleted gas reservoirs, which could be used as a monitoring strategy for the CO2 front and potential CO2 leakage. In addition to the solubility and concentration of the impurity gas would affect this phenomenon, there is a critical water saturation for the occurrence of significant chromatographic partitioning phenomenon associated with determined type and concentration of impurity gas. To increase the cost-effectiveness of CCS, the suitability of utilizing CO2 as the cushion gas in the UGSR was analyzed based on the geological parameters of Donghae depleted gas reservoir in Korea. The cyclic CH4 production and injection were conducted over a period of 15 years to acquire the mixing behavior of CO2 and CH4 in a relatively long-term period. The results showed that the maximum CO2 concentration that can be used for cushion gas is 9% under the condition of production and injection for 120 and 180 days in a production cycle at a rate of 4.05 and 2.7 kg/s, respectively. The typical curve of the mixing zone thickness can be divided into four stages, i.e., the increasing stage, smooth stage, suddenly increasing stage, and periodic change stage. The CO2 fraction in the UGSR, reservoir permeability, and production rate have a significant effect on the breakthrough of CO2 in the production well, while the effect of water saturation and temperature is neglectable. For the purpose of utilizing more CO2 as cushion gas in the UGSR, CO2 is supposed to be injected for supplementation during the operation of UGSR. Generally, the parametric uncertainty analysis conducted in this thesis is beneficial for the risk assessments in CCS projects. Co-injecting CO2 with impurities associated with CSEGR and utilizing CO2 as cushion gas in UGSR are favorable for improving the economic incentives of CCS operation. Therefore, this thesis is beneficial for promoting the application of CCS and mitigating the atmospheric CO2 emissions

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset