Geological modeling and reservoir simulation of Umiat: a large shallow oil accumulation

Thesis (M.S.) University of Alaska Fairbanks, 2013Current high oil price and availability of new technologies allow re-evaluation of oil resources previously considered uneconomic. Umiat oil field is one such resource: a unique, shallow (275-1055 feet), low-pressure (200-400 psi) reservoir within the permafrost zone located north of the Arctic Circle, 80 miles west of Trans Alaska Pipeline System (TAPS) with an estimated 1.5 billion barrel of oil-in-place. This thesis presents a reservoir model that incorporates recently identified permeability anisotropy patterns within the Cretaceous Nanushuk sandstone reservoir to evaluate various potential mechanisms such as horizontal wells and immiscible gas injections. The simulation model focuses on the Lower Grandstand which is identified as a better reservoir rock. The reservoir temperature is assumed at 26 OF and gas is injected at the same temperature to maintain equilibrium with the permafrost and prevent any well integrity problems. An optimum horizontal well length of 1500 ft was found and applied for all simulation cases. The simulation results show that with 50 years of lean gas injection, recovery factors for the base case and case of 600 psi injection pressures are 12% and 15%, respectively, keeping all other parameters constant.Chapter 1. Introduction -- 1.1. Overview -- 1.2. Objective of the study -- Chapter 2. Background -- 2.1. Field history and location -- 2.2. Previous research -- 2.3. Geologic modeling -- 2.4. Reservoir simulation -- 2.4.1. Incentives for reservoir simulation -- 2.4.2. Designing the simulation model -- 2.5. Horizontal wells -- 2.6. Gas injection -- 2.7. Gas hydrate -- Chapter 3. Geologic modeling, methodologies, and sources of data -- 3.1. Petrophysical property modeling -- 3.2. Permeability anisotropy -- 3.3. Optimal geologic grid design for simulation -- 3.4. Model geometry -- 3.5. Modeling of water saturation: concepts and challenges -- 3.6. Application of petrophysical cut-offs -- 3.7. Monte Carlo estimation of OOIP -- 3.8. Uncertainty parameter ranking by multiple realizations -- Chapter 4. Simulation model: preparation of input data for dynamic model -- 4.1. Rock and fluid data -- 4.1.1. Relative permeabilities to oil and gas -- 4.1.2. Relative permeabilities to oil and water -- 4.1.3. Capillary pressure -- 4.1.4. Fluid properties -- 4.2. Initializing model based on initial reservoir conditions -- 4.3. Design of production and injection wells -- Chapter 5. Results -- 5.1. Oil recovery by gas injection (base case) -- 5.2. Grid size optimization -- 5.3. Parameter optimization -- 5.3.1. Horizontal well length -- 5.3.2. Injection pressure -- 5.4. Sensitivity analysis -- 5.4.1. Permeability anisotropy -- 5.4.2. Relative permeability and end points saturations -- 5.4.3. Producing GOR -- 5.5. Discussion -- Chapter 6. Conclusions and recommendations -- 6.1. Conclusions -- 6.2. Recommendations -- References -- Nomenclature

Advancing Carbon Sequestration through Smart Proxy Modeling: Leveraging Domain Expertise and Machine Learning for Efficient Reservoir Simulation

Geological carbon sequestration (GCS) offers a promising solution to effectively manage extra carbon, mitigating the impact of climate change. This doctoral research introduces a cutting-edge Smart Proxy Modeling-based framework, integrating artificial neural networks (ANNs) and domain expertise, to re-engineer and empower numerical reservoir simulation for efficient modeling of CO2 sequestration and demonstrate predictive conformance and replicative capabilities of smart proxy modeling. Creating well-performing proxy models requires extensive human intervention and trial-and-error processes. Additionally, a large training database is essential to ANN model for complex tasks such as deep saline aquifer CO2 sequestration since it is used as the neural network\u27s input and output data. One major limitation in CCS programs is the lack of real field data due to a lack of field applications and issues with confidentiality. Considering these drawbacks, and due to high-dimensional nonlinearity, heterogeneity, and coupling of multiple physical processes associated with numerical reservoir simulation, novel research to handle these complexities as it allows for the creation of possible CO2 sequestration scenarios that may be used as a training set. This study addresses several types of static and dynamic realistic and practical field-base data augmentation techniques ranging from spatial complexity, spatio-temporal complexity, and heterogeneity of reservoir characteristics. By incorporating domain-expertise-based feature generation, this framework honors precise representation of reservoir overcoming computational challenges associated with numerical reservoir tools. The developed ANN accurately replicated fluid flow behavior, resulting in significant computational savings compared to traditional numerical simulation models. The results showed that all the ML models achieved very good accuracies and high efficiency. The findings revealed that the quality of the path between the focal cell and injection wells emerged as the most crucial factor in both CO2 saturation and pressure estimation models. These insights significantly contribute to our understanding of CO2 plume monitoring, paving the way for breakthroughs in investigating reservoir behavior at a minimal computational cost. The study\u27s commitment to replicating numerical reservoir simulation results underscores the model\u27s potential to contribute valuable insights into the behavior and performance of CO2 sequestration systems, as a complimentary tool to numerical reservoir simulation when there is no measured data available from the field. The transformative nature of this research has vast implications for advancing carbon storage modeling technologies. By addressing the computational limitations of traditional numerical reservoir models and harnessing the synergy between machine learning and domain expertise, this work provides a practical workflow for efficient decision-making in sequestration projects

The Genetics of Response to and Side Effects of Lithium Treatment in Bipolar Disorder: Future Research Perspectives

Although the mood stabilizer lithium is a first-line treatment in bipolar disorder, a substantial number of patients do not benefit from it and experience side effects. No clinical tool is available for predicting lithium response or the occurrence of side effects in everyday clinical practice. Multiple genetic research efforts have been performed in this field because lithium response and side effects are considered to be multifactorial endophenotypes. Available results from linkage and segregation, candidate-gene, and genome-wide association studies indicate a role of genetic factors in determining response and side effects. For example, candidate-gene studies often report GSK3β, brain-derived neurotrophic factor, and SLC6A4 as being involved in lithium response, and the latest genome-wide association study found a genome-wide significant association of treatment response with a locus on chromosome 21 coding for two long non-coding RNAs. Although research results are promising, they are limited mainly by a lack of replicability and, despite the collaboration of consortia, insufficient sample sizes. The need for larger sample sizes and “multi-omics” approaches is apparent, and such approaches are crucial for choosing the best treatment options for patients with bipolar disorder. In this article, we delineate the mechanisms of action of lithium and summarize the results of genetic research on lithium response and side effects

Genomic and neuroimaging approaches to bipolar disorder

BACKGROUND: To date, besides genome-wide association studies, a variety of other genetic analyses (e.g. polygenic risk scores, whole-exome sequencing and whole-genome sequencing) have been conducted, and a large amount of data has been gathered for investigating the involvement of common, rare and very rare types of DNA sequence variants in bipolar disorder. Also, non-invasive neuroimaging methods can be used to quantify changes in brain structure and function in patients with bipolar disorder. AIMS: To provide a comprehensive assessment of genetic findings associated with bipolar disorder, based on the evaluation of different genomic approaches and neuroimaging studies. METHOD: We conducted a PubMed search of all relevant literatures from the beginning to the present, by querying related search strings. RESULTS: ANK3, CACNA1C, SYNE1, ODZ4 and TRANK1 are five genes that have been replicated as key gene candidates in bipolar disorder pathophysiology, through the investigated studies. The percentage of phenotypic variance explained by the identified variants is small (approximately 4.7%). Bipolar disorder polygenic risk scores are associated with other psychiatric phenotypes. The ENIGMA-BD studies show a replicable pattern of lower cortical thickness, altered white matter integrity and smaller subcortical volumes in bipolar disorder. CONCLUSIONS: The low amount of explained phenotypic variance highlights the need for further large-scale investigations, especially among non-European populations, to achieve a more complete understanding of the genetic architecture of bipolar disorder and the missing heritability. Combining neuroimaging data with genetic data in large-scale studies might help researchers acquire a better knowledge of the engaged brain regions in bipolar disorder

Association of early life stress and cognitive performance in patients with schizophrenia and healthy controls

As core symptoms of schizophrenia, cognitive deficits contribute substantially to poor outcomes. Early life stress (ELS) can negatively affect cognition in patients with schizophrenia and healthy controls, but the exact nature of the mediating factors is unclear. Therefore, we investigated how ELS, education, and symptom burden are related to cognitive performance. The sample comprised 215 patients with schizophrenia (age, 42.9 ± 12.0 years; 66.0 % male) and 197 healthy controls (age, 38.5 ± 16.4 years; 39.3 % male) from the PsyCourse Study. ELS was assessed with the Childhood Trauma Screener (CTS). We used analyses of covariance and correlation analyses to investigate the association of total ELS load and ELS subtypes with cognitive performance. ELS was reported by 52.1 % of patients and 24.9 % of controls. Independent of ELS, cognitive performance on neuropsychological tests was lower in patients than controls (p < 0.001). ELS load was more closely associated with neurocognitive deficits (cognitive composite score) in controls (r = −0.305, p < 0.001) than in patients (r = −0.163, p = 0.033). Moreover, the higher the ELS load, the more cognitive deficits were found in controls (r = −0.200, p = 0.006), while in patients, this correlation was not significant after adjusting for PANSS. ELS load was more strongly associated with cognitive deficits in healthy controls than in patients. In patients, disease-related positive and negative symptoms may mask the effects of ELS-related cognitive deficits. ELS subtypes were associated with impairments in various cognitive domains. Cognitive deficits appear to be mediated through higher symptom burden and lower educational level

Extracellular vesicle approach to major psychiatric disorders

Over the last few years, extracellular vesicles (EVs) have received increasing attention as potential non-invasive diagnostic and therapeutic biomarkers for various diseases. The interest in EVs is related to their structure and content, as well as to their changing cargo in response to different stimuli. One of the potential areas of use of EVs as biomarkers is the central nervous system (CNS), in particular the brain, because EVs can cross the blood-brain barrier, exist also in peripheral tissues and have a diverse cargo. Thus, they may represent liquid biopsies of the CNS that can reflect brain pathophysiology without the need for invasive surgical procedures. Overall, few studies to date have examined EVs in neuropsychiatric disorders, and the present evidence appears to lack reproducibility. This situation might be due to a variety of technical obstacles related to working with EVs, such as the use of different isolation strategies, which results in non-uniform vesicular and molecular outputs. Multi-omics approaches and improvements in the standardization of isolation procedures will allow highly pure EV fractions to be obtained in which the molecular cargo, particularly microRNAs and proteins, can be identified and accurately quantified. Eventually, these advances will enable researchers to decipher disease-relevant molecular signatures of the brain-derived EVs involved in synaptic plasticity, neuronal development, neuro-immune communication, and other related pathways. This narrative review summarizes the findings of studies on EVs in major psychiatric disorders, particularly in the field of biomarkers, and discusses the respective therapeutic potential of EVs