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  Comparing Fishbone Drilling and Hydraulic Fracturing in Ultra-Low Permeability Geothermal Reservoirs

  Aimene Aihar, Nassim Bouabdallah, Ghoulem Ifrene, and Doina Irofti

  Harnessing geothermal energy through Enhanced Geothermal Systems (EGS) offers a sustainable and renewable means of tapping the Earth's subsurface heat. However, in ultra-low permeability formations like those found in the Williston Basin, the effectiveness of conventional hydraulic fracturing techniques is limited. This study evaluates the potential of fishbone drilling technology as an alternative approach, focusing on the efficiency and engineering aspects of both methods in the context of geothermal reservoirs with extremely low permeability.

  We carried out an extensive literature review, numerical simulations, and case studies to compare fishbone drilling and hydraulic fracturing in EGS applications. Fishbone drilling, which involves extending a single horizontal wellbore into multiple branching wellbores, shows several advantages in ultra-low permeability formations. This technique can effectively increase reservoir permeability and flow rates by accessing a larger volume of hot rock materials and creating an interconnected network of fractures, a challenge that hydraulic fracturing struggles to overcome in reservoirs with permeabilities as low as a few nanodarcies.

  Our analysis reveals that fishbone drilling can maintain wellbore stability in impermeable formations, where hydraulic fracturing might face difficulties in generating sufficient fractures without compromising wellbore integrity. Moreover, fishbone drilling can enhance fracture connectivity and heat extraction rates compared to hydraulic fracturing, making it a more efficient method for developing ultra-low permeability geothermal environments.

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  Salts Removal as an Effective and Economical Method of Bakken Formation Treatment

  Mousa Almousa, Olusegun Stanley Tomomewo, and Yeo Howe Lim

  One of the main aims of managing and containing waste disposal in deep rock formations is to safeguard individuals, the surroundings, and the groundwater reserves The elevated salt content of the water produced by the rock formation necessitated an analysis of its chemical composition, including its major ion content, in order to understand the characteristics of the rock Additionally, the total dissolved solids ( in the ND Bakken formation are greater than 300 g/l, which is much higher than the concentration of salt in seawater therefore, it is reasonable to propose a modified process to treat the salts found in this formation produced water Produced water in the unconventional U S Bakken oilfield has become a significant concern since oil and gas production growth has been substantial, and operating costs are increasing Reusing this considerable amount of produced water has become necessary since the treated water can be used for potable supplies, irrigation, deep well injection, maintenance, and fracking, which improves profits and mitigates groundwater pollution Several metals ( Ca, Mn, Sr, Li, and K) were extracted from the flow back water and water produced in the Bakken oilfield using lime, caustic soda, and soda ash at different dosages and pH values during this project The separation treatment using selective precipitation can be invaluable as a pre treatment process of desalination techniques Extracted salts are effective coagulants for removing various contaminants from wastewater therefore, the extracted Mg(OH) 2 and CaCO 3 were used for wastewater treatment and establish their efficiency in removing COD and the nutrients phosphorous and nitrogen from ND wastewater The recovery of these elements from produced water may create additional financial benefits for oil producing areas More importantly, this sustainable disposal of produced water may encourage the recycling and reuse practice, ultimately reducing the use of freshwater for hydraulic fracturing

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  Creation of a hyperplane device for horizontal cellular migration assays

  Nicholas M. Bittner, Nelofar Nargis, Ghoulem Ifrene, Brent Jeffrey Voels, and Colin K. Combs

  Cell culture studies routinely seek to monitor cell migration in response to chemoattractant stimuli. Common assays of cell migration employ well inserts and vertical cell migration assessment. This approach does not allow real-time monitoring of cell behavior. To address this need, we sought to develop a horizontal culture platform conducive to time course cell assessment changes in migration, morphology, phenotype etc. Modification of a commercial chamber slide allowed us to quantify cell migration in response to a 20% serum gradient. Based upon this finding, we designed and fabricated a prototype chamber slide for high replicate, real time assessment of cell migration in the serum gradient. The novel chamber slide design was effective for quantifying not only cell migration differences but visualizing cell movement. Optimization of the fabricated design will provide a novel tool for cell biology research.

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  Machine Learning Algorithms for Predicting Liquid Loading in Gas Wells

  Nassim Bouabdallah, Abdeldjalil Latrach, Aimene Aihar, and Adesina Fadairo

  Liquid loading is a term used to describe the situation where the gas produced from a well is unable to carry the liquid that is also produced along with it (Khetib et al., 2022). As a result, the liquid starts to accumulate in the wellbore. This accumulation of liquid can cause a decrease in gas production and in severe cases, it may even lead to a complete stoppage of production (Khetib et al., 2022, 2023) . The phenomenon of liquid loading in gas wells occurs when the critical gas velocity is less than a certain value, leading to a decrease in gas flow rate and ultimately a decrease in production (Merzoug et al., 2022). To simulate this phenomenon and study it in detail, we conducted experiments in a multiphase flow loop. We aimed to compare the results of the experiment with machine learning algorithms to predict the loading and unloading of the well. The purpose of this experiment was to examine the start of liquid accumulation in a gas well using a 2.4 meter vertical rigid pipe system with a 0.0508 meter (2 inch) internal diameter (Khetib. Y, 2022). The study analyzed the flow of gas and liquid in a vertical direction to gain insight into how liquid builds up in a vertical tube as gas flow decreases. We varied the gas and liquid flow rates to simulate different conditions and recorded the pressure and flow rate data (Khetib. Y, 2022). We used this data to train machine learning algorithms such as Support Vector Machines (SVMs) (Ifrene et al., 2023), Random Forests, XGBoost, and Neural Networks, to predict whether the well is loaded or unloaded. We then compared the predictions of the machine learning algorithms with the experimental data.

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  Acoustic Detection of UAS With Edge Analytics

  Jacob Hagberg, Prasad Pothana, Akshay Ram Ramchandra, Paul Snyder, and Sreejith Nair

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  Advancing our Understanding of Non-linear Flow Behavior in X-Crossing Fractures through 3D Printing Technology

  Ghoulem Ifrene, Sven Egenhoff, Doina Irofti, Nicholas Bittner, and Tyler Newman

  Fractured reservoir systems play a critical role in the efficient management of hydrocarbon resources, CO2 storage, and geothermal energy extraction. Understanding the behavior and characteristics of these systems is essential for optimizing recovery, reducing carbon footprint, and increasing efficiency. However, fractures within reservoir systems create complex geometries and can make fluid flow behavior complex and difficult to predict. Replicating these complex geometries in the laboratory has been challenging, but advancements in 3D printing technology have made it possible to create accurate models of rough-walled fracture geometries. In this study, the impact of geometric characteristics on fluid flow behavior in connected fractures was investigated using 3D-printed specimens with X-junction shapes, different roughness, intersection angles, and apertures. The experimental study was conducted using core flooding, and sensitivity analysis was performed on sixteen specimens to determine critical parameters affecting fluid flow behavior. Results showed that intersection angle had a significant impact on fluid flow behavior, with higher angles presenting more restriction than lower angles. Furthermore, the roughness and the aperture are affecting the fluid flow behavior dramatically, thus the increasing roughness and decreasing aperture create more restrictions to the fluid flow. The experiments suggest that fracture permeability estimation is greatly influenced by the angle at which fractures intersect. Fractures with low-angle intersections exhibit higher permeability than those with high-angle intersections. These findings provide valuable insights into the fluid flow behavior in complex fracture geometries and demonstrate the potential of 3D printing technology in paving the way for future research in such systems.

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  Evaluation of Hybrid Prediction Models for Accurate Rate of Penetration (ROP) Prediction in Drilling Operations

  Abdelhakim Khouissat, Mohamed Riad Youcefi, Ghoulem Ifrene, and Doina Irofti

  The precise prediction of the rate of penetration (ROP) is of utmost importance for optimizing drilling operations and minimizing costs while increasing efficiency. However, the complex and nonlinear nature of the drilling process can pose significant challenges in achieving accurate ROP predictions. To address this challenge, multiple hybrid prediction models have been developed, and their accuracy in ROP prediction has been compared.

  To accomplish this objective, we created three different hybrid models, including Artificial Neural Network – Genetic Algorithm (ANN-GA), Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), and Support Vector Regression (SVR) to estimate ROP. These models were trained and tested using drilling data collected from surface sensors, including drilling parameters such as weight on bit (WOB), revolutions per minute (RPM), flow rate, ROP, and drilling torque.

  The hybrid models were able to accurately estimate the ROP for the given drilling conditions and lithologies by utilizing these parameters. Furthermore, the models' accuracy and effectiveness were assessed by training and testing them using the collected drilling data.

  Upon evaluating the performance of the three algorithms, our study shows that SVR (Support Vector Regression) outperformed ANN (Artificial Neural Network) in accuracy and precision when predicting the target variable. SVR consistently provided more accurate and precise predictions, capturing the underlying patterns in the data effectively. While ANN-GA (Artificial Neural Network with Genetic Algorithm) performed better than ANN-PSO (Artificial Neural Network with Particle Swarm Optimization) in the training dataset, it exhibited lower accuracy during testing. This highlights the importance of evaluating algorithm performance in both training and testing scenarios. The results also emphasize that complexity doesn't always lead to better predictions. SVR offers a promising choice for accurate and reliable predictions, but further research is needed to explore the contrasting performances and optimize these algorithms.

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  Exploring Underground Hydrogen Storage Options in North Dakota: A Review

  Aimen Laalam and Olusegun Stanley Tomomewo

  As the world shifts towards a low-carbon future, the demand for efficient, safe, and cost-effective energy storage solutions has become increasingly critical. Hydrogen has emerged as a promising energy carrier with numerous advantages, such as high energy density, zero emission combustion, and versatile applications. Nevertheless, the challenge of effective hydrogen storage remains. This study examines the potential of underground hydrogen storage (UHS) in North Dakota, assessing its opportunities and challenges in supporting the region's renewable energy objectives. North Dakota's unique geological features, abundant renewable energy resources, and growing energy demands make it an ideal location for UHS implementation. This review explores various UHS technologies, including salt caverns, depleted oil and gas reservoirs, and aquifers, emphasizing their technical feasibility, environmental impacts, and economic viability within the North Dakota context. Salt caverns, created in subsurface salt formations, are well-suited for UHS due to their impermeability, structural integrity, and rapid cycling capacity. North Dakota's plentiful salt deposits, especially in the Williston Basin, present significant opportunities for large-scale hydrogen storage. Depleted oil and gas reservoirs offer another feasible option, leveraging existing infrastructure and reservoir knowledge. The state's long history of oil and gas production yields numerous depleted reservoir candidates for potential UHS projects. Aquifers, naturally occurring underground water-bearing formations, constitute a third alternative. Although less investigated than salt caverns and depleted reservoirs, aquifers show promise for UHS in North Dakota due to their extensive distribution and potential for substantial storage capacities. Additionally, we emphasize key economic factors and benefits for the state. In conclusion, this study provides a comprehensive assessment of the opportunities and challenges linked to implementing underground hydrogen storage in North Dakota. By conducting a detailed analysis of the region's geological characteristics, economic factors, and environmental concerns, we aim to offer valuable insights for policymakers, industry stakeholders, and researchers. This information can help inform future UHS projects and support the state's transition towards a sustainable energy future.

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  UAS Risk Analysis In And Around Airports

  Prasad Pothana, Jack Thornby, Michael Ullrich, Sreejith Vidhyadharan, and Paul Snyder