Impact of Well Placement in the Fractured Geothermal Reservoirs Based on Available Discrete Fractured System

Well placement in a given geological setting for a fractured geothermal reservoir is necessary for enhanced geothermal operations. High computational cost associated with the framework of fully coupled thermo-hydraulic-mechanical (THM) processes in a fractured reservoir simulation makes the well positioning a missing point in developing a field-scale investigation. To enhance the knowledge of well placement for different working fluids, we present the importance of this topic by examining different injection-production well (doublet) positions in a given fracture network using coupled THM numerical simulations. Results of this study are examined through the thermal breakthrough time, mass flux, and the energy extraction potential to assess the impact of well position in a two-dimensional reservoir framework. Almost ten times the difference between the final amount of heat extraction is observed for different well positions but with the same well spacing and geological characteristics. Furthermore, the stress field is a strong function of well position that is important concerning the possibility of high-stress development. The objective of this work is to exemplify the importance of fracture connectivity and density near the wellbores, and from the simulated cases, it is sufficient to understand this for both the working fluids. Based on the result, the production well position search in the future will be reduced to the high-density fracture area, and it will make the optimization process according to the THM mechanism computationally efficient and economical.

Prospects for the application of proppant hydraulic fracturing at Kashiro-Verey operational facilities Volga-Ural oil and gas province

The geological structure of Kashiro-Verey carbonate deposits is considered on the example of one of the deposits of the Perm Region. By combining geophysical studies of wells, standard and tomographic studies of core, the following lithotypes of carbonate rocks were identified: highly porous cavernous, layered heterogeneous porous, heterogeneous fractured porous, dense. It was found that for heterogeneous lithotypes, the porosity estimate in the volume of the permeable part of the rocks significantly exceeds 7%. Experiments on the destruction of rocks were carried out for the selected lithotypes. As a result, it was found that cracks do not form for samples of the cavernous lithotype at a compression pressure of 20 MPa. For a compacted lithotype, already at a compression pressure of more than 10 MPa, an intensive development of fracturing occurs. As a result of multiaxial loading of cores, which can be considered as analogous fracturing of the formation, wide fractures are formed, along which filtration of fluids can occur. Keywords: proppant hydraulic fracturing; X-ray tomography of the core; porosity; permeability; fractured reservoir; oil deposit; carbonate deposits.

Optimization of hydraulic fracturing treatment in tight sand reservoir of lower indus basin: an integrated approach

An approach for post-frac production profiling has been presented in this study by integrating a fracture model with a reservoir simulation model for a well drilled in tight sand reservoir of Lower Indus Basin in Pakistan. The presented integrated approach couples the output from the fracture growth model with a reservoir simulation model to effectively predict the behavior of a fractured reservoir. Optimization of hydraulic fracturing was done efficiently through the work presented in this study. The integrated model was used to perform various sensitivities. The production profiles obtained for each case were subsequently used to determine the most profitable case, using an economic model.

The effect of gravity drainage mechanism on oil recovery by reservoir simulation; a case study in an Iranian highly fractured reservoir

A comprehensive reservoir simulation study was performed on an oil field that had a wide fracture network and could be considered a typical example of highly fractured reservoirs in Iran. This field is located in southwest of Iran in Zagros sedimentary basin among several neighborhood fields with relatively considerable fractured networks. In this reservoir, the pressure drops below the saturation pressure and causes the formation of a secondary gas cap. This can help to better assess the gravity drainage phenomenon. We decided to investigate and track the effect of gravity drainage mechanism on the recovery factor of oil production in this field. In this study, after/before the implementation of gas injection scenarios with different discharges, the contribution of gravity drainage mechanism to the recovery factor was found more than 50%. Considering that a relatively large number of studies have been conducted on this field simultaneously with the growth of information from different aspects and this study is the last and most comprehensive study and also the results are extracted from real field data using existing reservoir simulators, it is of special importance and can be used by researchers.

Uncertainty Quantification and Optimization of Deep Learning for Fracture Recognition

Automatic fracture recognition from borehole images or outcrops is applicable for the construction of fractured reservoir models. Deep learning for fracture recognition is subject to uncertainty due to sparse and imbalanced training set, and random initialization. We present a new workflow to optimize a deep learning model under uncertainty using U-Net. We consider both epistemic and aleatoric uncertainty of the model. We propose a U-Net architecture by inserting dropout layer after every "weighting" layer. We vary the dropout probability to investigate its impact on the uncertainty response. We build the training set and assign uniform distribution for each training parameter, such as the number of epochs, batch size, and learning rate. We then perform uncertainty quantification by running the model multiple times for each realization, where we capture the aleatoric response. In this approach, which is based on Monte Carlo Dropout, the variance map and F1-scores are utilized to evaluate the need to craft additional augmentations or stop the process. This work demonstrates the existence of uncertainty within the deep learning caused by sparse and imbalanced training sets. This issue leads to unstable predictions. The overall responses are accommodated in the form of aleatoric uncertainty. Our workflow utilizes the uncertainty response (variance map) as a measure to craft additional augmentations in the training set. High variance in certain features denotes the need to add new augmented images containing the features, either through affine transformation (rotation, translation, and scaling) or utilizing similar images. The augmentation improves the accuracy of the prediction, reduces the variance prediction, and stabilizes the output. Architecture, number of epochs, batch size, and learning rate are optimized under a fixed-uncertain training set. We perform the optimization by searching the global maximum of accuracy after running multiple realizations. Besides the quality of the training set, the learning rate is the heavy-hitter in the optimization process. The selected learning rate controls the diffusion of information in the model. Under the imbalanced condition, fast learning rates cause the model to miss the main features. The other challenge in fracture recognition on a real outcrop is to optimally pick the parental images to generate the initial training set. We suggest picking images from multiple sides of the outcrop, which shows significant variations of the features. This technique is needed to avoid long iteration within the workflow. We introduce a new approach to address the uncertainties associated with the training process and with the physical problem. The proposed approach is general in concept and can be applied to various deep-learning problems in geoscience.

Fractures and Flow Patterns Detection in Carbonate Reservoirs Using Intelligent Sensor Selection in a Deep Learning and Uncertainty Framework

4th Industrial Revolution (4IR) technologies have assumed critical importance in the oil and gas industry, enabling data analysis and automation at unprecedented levels. Formation evaluation and reservoir monitoring are crucial areas for optimizing reservoir production, maximizing sweep efficiency and characterizing the reservoirs. Automation, robotics and artificial intelligence (AI) have led to tremendous transformations in these areas, in particular in subsurface sensing. We present a novel 4IR inspired framework for the real-time sensor selection for subsurface pressure and temperature monitoring, as well as reservoir evaluation. The framework encompasses a deep learning technique for sensor data uncertainty estimation, which is then integrated into an integer programming framework for the optimal selection of sensors to monitor the reservoir formation. The results are rather promising, showing that a relatively small numbers of sensors can be utilized to properly monitor the fractured reservoir structure.

Characterization of Critically Stressed Fractures Using Fluid-Flow Models for Naturally Fractured Reservoirs

Fracture characterization, including permeability and deformation due to fluid flow, plays an essential role in hydrocarbon production during the development of naturally fractured reservoirs. The conventional way of characterization of the fracture is experimental, and modeling approaches. In this study, a conceptual model will be developed based on the structural style to study the fracture distributions, the influence of the fluid flow and geomechanics in the fracture conductivity, investigate the stress regime in the study area. Understanding the fracture properties will be conducted by studying the fracture properties from the core sample, image log interpretation. 3D geomechanical models will be constructed to evaluate the fluid flow properties; the models consider the crossflow coefficient and the compression coefficient. According to the model results, the fracture permeability decreases with increasing effective stress. The degree of decline is related to the crossflow coefficient and the compression coefficient. Most of these reservoirs are mainly composed of two porosity systems for fluid flow: the matrix component and fractures. Therefore, fluid flow path distribution within a naturally fractured reservoir depends on several features related to the rock matrix and fracture systems' properties. The main element that could help us identify the fluid flow paths is the critical stress analysis, which considers the in-situ stress regime model (in terms of magnitude and direction) and the spatial distributions of natural fractures fluid flow path. The critical stress requires calculating the normal and shear stress in each fracture plane to evaluate the conditions for critical and non-critical fractures. Based on this classification, some fractures can dominate the fluid-flow paths. To perform the critical stress analysis, fracture characterization and stress analysis were described using a 3D stress tensor model capturing the in-situ stress direction and magnitude applied to a discrete fracture model, identifying the fluid flow paths along the fractured reservoir. The results show that in-situ stress rotation observed in the breakouts or drilling induce tensile fractures (DITFs) interpreted from borehole images. The stress regime changes are probably attributed to some influence of deeply seated faults under the studied sequence. the flow of water-oil ratio through intact rock and fractures with/without imbibition was modeled based on the material balance based on preset conceptual reservoir parameters to investigate the water-oil ratio flow gradients

Advanced Engineering Solutions to Achieve Best in Class Performance in Drilling Extended Reach Dual Lateral Wells in the Sultanate of Oman

A Large Omani Operator successfully achieved best in class performance in drilling extended reach dual-lateral wells in Oman. Turning the legs to achieve the required separation distance and continue drilling to the required depth through a thin fractured reservoir resulted in complex well trajectories and harsh drilling environment. This paper will focus on the newly innovative designs, engineering optimizations and utilizing lean methodology to overcome drilling risks and achieve best in class performance. Rotary Steerable system was utilized to drill the extended reach drilling (ERD) in 3D with continuous proportional steering technology. Advance modeling including lateral shocks, Torque and Drag and BHA design were as well key enablers. Logging while drilling tools supported reservoir mapping and real-time well placement decisions. To excel in lateral applications and overcome harsh drilling environment, a shallow cone tip profile with High Performance cutter bit technology was selected. A focus optimization project using lean tools was performed to map out the undercut process, visualize possible waste, perform root causes analysis and implement countermeasures to eliminate the process waste Regional benchmark showed that the performance of 11 wells drilled since the start of the campaign is located within the best 10% of the benchmark data which is marked as best in class performance. Due to the continues improvement, the campaign manages to reach a learning curve of 30%. Furthermore, the actual production from the wells was 300% more than the forecast. Using the advanced RSS and bit technologies resulted in reducing the Torque values in the lateral section by 30% which effectively increased the reservoir drilled interval by 22%. The designed BHA also managed to complete wells including multi undercuts (up to 6) in one run. One trip Whipstock System for creating the second leg is used as part of the well design. The Whipstock system which is uniquely set in the horizontal tangent section has achieved 100% success rate in setting and retrieving operations. The undercut activities have improved by 50% as a direct result of the optimization Lean project. In addition, utilizing lean methodology resulted in reducing the cost impact of the additional sidetracks (undercuts) which enabled having best reservoir quality and achieving savings over the total cost of ownership TCO. Extended Reach Dual lateral well design was utilized for the first time in PDO operations during this drilling campaign. This paper will present how advance modelling can enable the industry to deliver complex well designs. Additionally, it will introduce the company innovation in implementing the Lean philosophy to optimize the drilling operation.