Neural network applications to reservoirs: Physics-based models and data models

International audienceEditoria

Fuzzy Sets Applications in Civil Engineering Basic Areas

Civil engineering is a professional engineering discipline that deals with the design, construction, and maintenance of the physical and naturally built environment, including works like roads, bridges, canals, dams, and buildings. This paper presents some Fuzzy Logic (FL) applications in civil engeering discipline and shows the potential of facilities of FL in this area. The potential role of fuzzy sets in analysing system and human uncertainty is investigated in the paper. The main finding of this inquiry is FL applications used in different areas of civil engeering discipline with success. Once developed, the fuzzy logic models can be used for further monitoring activities, as a management tool

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

Dynamic data driven investigation of petrophysical and geomechanical properties for reservoir formation evaluation

Petrophysical and geomechanical properties of the formation such as Young’s modulus, bulk modulus, shear modulus, Poisson’s ratio, and porosity provide characteristic description of the hydrocarbon reservoir. It is well-established that static geomechanical properties are good representatives of reservoir formations; however, they are non-continuous along the wellbore, expensive and determining these properties may lead to formation damage. Dynamic geomechanical formation properties from acoustic measurements offer a continuous and non-destructive means to provide a characteristic description of the reservoir formation. In the absence of reliable acoustic measurements of the formation, such as sonic logs, the estimation of the dynamic geomechanical properties becomes challenging. Several techniques like empirical, analytical and intelligent systems have been used to approximate the property estimates. These techniques can also be used to approximate acoustic measurements thus enable dynamic estimation of geomechanical properties. This study intends to explore methodologies and models to dynamically estimate geomechanical properties in the absence of some or all acoustic measurements of the formation. The present work focused on developing empirical and intelligent systems like artificial neural networks (ANN), Gaussian processes (GP), and recurrent neural networks (RNN) to determine the dynamic geomechanical properties. The developed models serve as a cost-effective, reliable, efficient, and robust methods, offering dyanmic geomechanical analysis of the formation. This thesis has five main contributions: (a) a new data-driven empirical model of estimating static Young’s modulus from dynamic Young’s modulus, (b) a new data-driven ANN model for sonic well log prediction, (c) a new data-driven GP model for shear wave transit time prediction, (d) a new dynamic data-driven RNN model for sonic well log reproduction, and (e) an assessment on the ANN as a reliable sonic logging tool

Earthquake Engineering

The book Earthquake Engineering - From Engineering Seismology to Optimal Seismic Design of Engineering Structures contains fifteen chapters written by researchers and experts in the fields of earthquake and structural engineering. This book provides the state-of-the-art on recent progress in the field of seimology, earthquake engineering and structural engineering. The book should be useful to graduate students, researchers and practicing structural engineers. It deals with seismicity, seismic hazard assessment and system oriented emergency response for abrupt earthquake disaster, the nature and the components of strong ground motions and several other interesting topics, such as dam-induced earthquakes, seismic stability of slopes and landslides. The book also tackles the dynamic response of underground pipes to blast loads, the optimal seismic design of RC multi-storey buildings, the finite-element analysis of cable-stayed bridges under strong ground motions and the acute psychiatric trauma intervention due to earthquakes

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

Developing a risk assessment model using fuzzy logic to assess groundwater contamination from hydraulic fracturing

Technological advances in directional drilling has led to rapid exploitation of onshore unconventional hydrocarbons using a technique known as hydraulic fracturing. This process took off initially in the US, with Canada following closely behind, but brought with it controversial debates over environmental protection, particularly in relation to groundwater contamination and well integrity failure. Prospective shale gas regions lie across areas in Europe but countries such as the UK are facing public and government turmoil surrounding their potential exploitation. This extent of energy development requires detailed risk analysis to eliminate or mitigate damage to the natural environment. Subsurface energy activities involve complex processes and uncertain data, making comprehensive, quantitative risk assessments a challenge to develop. A new, alternative methodology was applied to onshore hydraulic fracturing to assess the risk of groundwater contamination during well injection and production. The techniques used deterministic models to construct failure scenarios with respect to groundwater contamination, stochastic approaches to determine component failures of a well, and fuzzy logic to address insufficiency or complexity in data. The framework was successfully developed using available data and regulations in British Columbia (BC), Canada. Fuzzy Fault Tree Analysis (FFTA) was demonstrated as a more robust technique compared with conventional Fault Tree Analysis (FTA) and implemented successfully to quantify cement failure. A collection of known risk analysis methods such as Event Tree Analysis (ETA), Time at Risk Failure (TRF) and Mean Time To Failure (MTTF) models were successfully applied to well integrity failure during injection, with the novel addition of quantifying cement failures. An analytical model for Surface Casing Pressure (SCP) during well production highlighted data gaps on well constructions so a fuzzy logic model was built to a 93% accuracy to determine the location of cement in a well. This novel application of fuzzy logic allowed the calculation of gas flow rate into an annulus and hence the probability of well integrity failure during production using ETA. The framework quantified several risk pathways across multiple stages of a well using site-specific data, but was successfully applied to a UK case study where there existed significant differences in geology, well construction and regulations. The application required little extra work and demonstrated the success and limitations of the model and where future work could improve model development. This research indicated that risks to groundwater from hydraulic fracturing differ substantially depending on well construction. Weighing up the risk to groundwater compared with financial gain for well construction will be essential for decision-makers and policy. To reduce the social anxiety of hydraulic fracturing in the UK, decision-makers who face criticism must ensure information is disseminated properly to the public with a well-defined risk analysis which can be interpreted easily without prerequisite knowledge. Finally, although this research is based on onshore hydraulic fracturing, the risk assessment techniques are generic enough to allow application of this research to other subsurface activities such as CO2 sequestration, waste injection disposal and geothermal energy.Engineering and Physical Sciences Research Council (EPSRC