As electrical grids increasingly rely on variable renewable energy, maintaining reliability and cost efficiency becomes more complex. To address these challenges, this study analyzed the integration of geothermal district heating as a grid-responsive thermal resource within a microgrid in Tuttle, Oklahoma. Building energy modeling using EnergyPlus estimated annual district heating demand at 2.9 GWh, with a peak load of 2.8 MWth. Technoeconomic analyses were conducted to meet the heating demand under three geothermal scenarios, varying by production depth, flow rate, and thermal output, each supplemented by natural gas peaking boilers. In parallel, equivalent electrical load profiles were developed using typical coefficients of performance (COPs) for air-source heat pumps and electric boilers to establish an electrified baseline scenario. A complete end-use electrical load profile was also developed for the microgrid using Cambium dataset. The modeling results demonstrated reliable and economic operation of the geothermal systems over 30 years, with COPs ranging from 2.6 to 8.9 and the lowest levelized heating cost at $54.6/MWh. Geothermal integration reduced electricity consumption by up to 94.7% compared to the non-geothermal base case, yielding annual energy savings of up to $803 k. Avoided grid costs ranged from $65 k-$147 k per year, with individual events avoiding up to $4,863 per hour. Grid-responsive operation further reduced wholesale energy costs by 53-56 %. These findings demonstrate geothermal heating, traditionally treated as a non-grid-responsive thermal resource, can be reconfigured to support dynamic grid services, offering a scalable pathway to enhance reliability and reduce costs in renewable-rich microgrids and district heating networks.

Abstract Repurposing oil and gas wells for geothermal is an opportunity to extend their useful lifetime. Injection of cold fluids into the subsurface alters the three-dimensional stress state and induces poroelastic and thermoelastic deformation within reservoir rocks. Understanding these coupled responses is critical for assessing geomechanical stability, which indirectly affects temperature and enthalpy production in geothermal systems and potential risks associated with these operations. This study investigates stress and deformation using a thermo-hydro-mechanical (THM) modeling framework for geothermal operations at the DOE-funded project "Intelligent Repurposing of Hydrocarbon Wells System to Harness the Geothermal Potential of Oklahoma Sedimentary Basin" which investigates direct heat usage for three district Schools in Tuttle, OK. We develop a three-dimensional numerical model to simulate reservoir-scale stress and deformation driven by pressure and temperature changes as a result of geothermal cycling within the Haskell formation, between two operational wells that are ~1,000 ft apart. Pressure and temperature histories from reservoir simulations are applied as inputs to the geomechanical model, with mechanical deformation evaluated in a one-way coupled framework without feedback to flow processes. Initial stress conditions are calibrated using history-matched leak-off test data to establish realistic pre-injection stress states. Our model incorporates well logs, reservoir mechanical properties, and operational constraints representative of field conditions within a strike-slip faulting regime. Operational scenarios are examined across both short- and long-term production periods (1 and 25 years) and variable flow rates ranging from 2,000 to 5,000 barrels per day. Results reveal distinct spatial and temporal patterns of stress redistribution and deformation within the reservoir and surrounding formations. Long-term and higher-rate scenarios are associated with more pronounced stress perturbations and deformation responses than short-term operations. This integrated mechanical response informs safe operating injection-rate thresholds for geothermal enthalpy production that produce minimal ground deformation and stress changes, ensuring safe operations. The workflow provides a practical approach for evaluating geomechanical response under realistic operational scenarios, supporting geothermal project planning and risk assessment.

Repurposing abandoned oil and gas infrastructure for geothermal energy production has great potential to reduce greenhouse gas (GHG) emissions. This study quantified the life cycle global warming potential of geothermal energy production using four inactive oil and gas wells repurposed for district heating in Tuttle, Oklahoma. A cradle-to-grave prospective life cycle assessment was performed to compare GHG emissions between the geothermal district heating system and conventional natural gas-fired heating system from 2020 to 2050. For initial implementation of the geothermal system, we investigated two approaches: 1) repurposing abandoned infrastructure from a nearby oil and gas well site, and 2) production and injection well drillings including new construction of a central heat exchange station. Environmental impacts from the geothermal system were estimated for five scenarios where a natural gas peaking boiler is incorporated to supply peak heat demand. The prospective results indicated that cumulative reduction in GHG emissions from transitioning to the geothermal district heating system increase over time as a function of future renewable resource penetration and technological advancements within electricity, fuel, and steel production. Over 30 years, the global warming potential associated with the district heating demand will have been reduced by up to 24 % with the repurposed system. These results imply that repurposing existing oil and gas infrastructure for geothermal energy systems of district heating will bring future climate benefits.

Rate of penetration (ROP) modeling has been widely employed to improve drilling efficiency, aiming to reduce both operational costs and risks. This study presents a hybrid model for predicting ROP by combining analytical and data-driven approaches, aimed at enhancing drilling efficiency in challenging formations. The model integrates operational parameters, rock strength properties, and bit design factors, using machine learning (ML) to estimate real-time rock strength at the bit for each depth interval by predicting compressional wave velocity and lithology. These predictions facilitate the calculation of uniaxial compressive strength (UCS) and confined compressive strength (CCS), inputs for ROP estimation. The study included datasets from five wells in the Norwegian Continental Shelf, with four wells used for model training and one for model testing/validation (blind test). The Random Forest regression model achieved an R2 value of 93% for compressional wave velocity predictions, while the Random Forest classification model attained 96% accuracy in lithology prediction during blind testing. Model validation showed a strong correlation between calculated and measured ROP values, underscoring its accuracy. Sensitivity analysis was performed to evaluate the influence of various parameters, such as weight on bit (WOB), revolutions per minute (RPM), drilling fluid density, flow rate, bit hydraulics, and CCS, highlighting their interdependent effects on ROP. The sensitivity analysis indicated that CCS, WOB, RPM and bit diameter have the greatest impact on ROP. Existing ROP models lack real-time integration of lithology and compressional wave velocity at the bit, limiting their ability to estimate rock strength. This study addresses these gaps by incorporating ML to predict these parameters at each depth interval, enhancing unconfined and confined compressive strength calculations used in the ROP model. The results highlight the importance of optimizing drilling parameters to maximize ROP and operational efficiency, indicating the hybrid model's potential for real-time applications in complex drilling environments.

Borehole stability is critical in drilling, particularly in shale formations, where failures can cause significant challenges. Traditional models for borehole stability rely on empirical rock strength correlations using logging-while-drilling (LWD) data, often unavailable in top-hole sections or recorded far above the bit. This paper introduces a real-time advisory system that integrates machine learning (ML) and porothermochemoelastic analysis to dynamically calculate key parameters like minimum drilling fluid density (MDFD) and breakout depth. The system predicts lithology and DTCO at the bit in real-time, using ML algorithms trained on drilling parameters and gamma-ray data from Norwegian continental shelf wells. Lithology prediction achieved accuracies of 86% for entire wells and 95% for reservoir sections, while DTCO predictions showed an R2 of 92%. These predictions enabled rock mechanical property estimates, such as UCS and flow factor, with accuracies of 90% and 92%, respectively. The porothermochemoelastic model predicted an MDFD of 1.13 g/cc, slightly lower than the field value of 1.14 g/cc, suggesting potential optimization of drilling fluid usage. Compared to conservative estimates from linear elastic (1.27 g/cc) and poroelastic (1.20 g/cc) models, the integrated approach accounts for thermal and chemical effects, offering enhanced accuracy and efficiency in borehole stability analysis.

Real-time drilling analysis requires knowledge of lithology at the bit. However, logging while drilling (LWD) sensors in the bottom hole assembly (BHA) are usually 2–50 m (7–164 ft) away from the bit (called the sensor offset), resulting in a lag in the real-time drilling analysis. In this study, we propose using both drilling and petrophysical parameters to predict the lithology at the bit in real time. This study explored three methodologies for predicting lithology. The first is a conventional approach that uses bulk density and porosity cross plots. The second combines unsupervised and supervised machine learning (ML), which begins with clustering algorithms such as k-means and hierarchical clustering to categorize rock types, followed by the application of classification algorithms, including K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), multi-layer perceptron (MLP), and deep learning (DL) for prediction. The third method directly applies classification algorithms to manually labeled lithology data for rock-type prediction. ML-based methodologies are expected to yield more accurate and timely predictions than conventional methods, potentially improving real-time drilling analysis. Initially, the conventional bulk density vs. porosity cross-plot method was employed, resulting in an accuracy of 58% with blind-well test data. Second, the ML approach exhibited an improvement over the cross-plot method, with an accuracy of 66%. The third approach with the RF classification resulted in a significant increase in accuracy, attaining an accuracy rate of 86%. The findings indicate that both ML models, specifically the classification-only approach (third method), outperformed conventional bulk density and porosity cross-plots in lithology prediction. The results of this analysis indicate an algorithm that improves real-time decision-making during the drilling process, reduces the limitations of the LWD sensor offset, and ensures optimal drilling performance.

This project developed and validated (through field tests) a new low-cost all-digital pressure sensing technology for in situ distributed downhole pressure monitoring in unconventional oil and gas (UOG) fields. The all-digital sensing technology uses a built-in non-electric analog-to-digital converter (ADC) to transform the pressure information into a combination of binary (ON/OFF) states. As such, the system does not need downhole electronics for signal conditioning and telemetry. The all-digital sensors can be remotely logged over a long distance, and many sensors can be multiplexed for distributed sensing. Based on a review of unconventional wells in the Lower 48 states, the specification of the sensor is to measure pressure up to 69 MPa (10,000 psi) and temperature up to 250°C. A sensor with a helical bourdon sensing element and a digital signal decoder of 50 mm diameter and 109 mm length was constructed. The helical bourdon sensing element was made of 304L stainless steel and filled with motor oil. The digital converter was made up of 8 digital reading pads constructed of high-temperature epoxy with conductive inserts made of stainless steel. The sensor had a linear response to pressure with an accuracy of 0.14 MPa (20 psi). To withstand the high pressure, the sensor was enclosed in a stainless-steel pressure housing with a wall thickness of 5.5 mm, a diameter of 73 mm, and a length of 724 mm. In the laboratory tests, the sensor exhibited no temperature-related effects on the results. The sensor did not show drift over a 14-day test period at elevated pressure. A field test was conducted where the sensor was deployed in a test wellbore at the Quest drilling test facility to a depth of 0 feet over three weeks. The sensor was attached to the production rods, along with a downhole reference sensor of PPS27 type, which is a permanent downhole monitoring system. During the testing phase, the test well annular blow-out preventer was closed, and the well was pressurized at the surface to 11 MPa (1600 psi). The sensor read the elevated bottom hole pressure of 1500 psi. A multiplexing unit was created for the sensor to deploy multiple sensors on one data transmission line in a distributed approach. The multiplexing unit was tested in a simulated environment of 3048 m (10,000 ft) with five sensors distributed. The sensors were pressurized at different intervals. The multiplexed sensors recorded the correct pressure, and the multiplexing did not interfere with the readings. The proposed concept of an all-digital pressure sensor for harsh downhole environments was designed, manufactured, and tested in the laboratory and tested at the field to a up to 69 MPa and 250°C. This technology has high-temperature tolerance and has potential in downhole areas outside oil and gas, such as carbon capture and storage (CCS) and geothermal wells. The sensor concept has been proven in this project, but to create a commercially viable product, manufacturing a sensor with a smaller diameter needs to be performed.

Polycrystalline diamond compact (PDC) bits are superior for drilling geothermal wells because of their superior drilling performance compared to conventional roller cone bits. However, the shear action of PDC bits generates detrimental vibrations during drilling. The main objective of this study was to establish a methodology to analyze and predict the stick-slip severity in hard rocks for geothermal wells. Two non-linear coupled axial-torsional bit-rock interaction (BRI) models are presented: one is based on a velocity-decaying friction model (VDF), and the other is based on a state-dependent delay friction model (SDDF). The capabilities of the two models were evaluated to assess the axial and torsional dynamic stabilities of drill stems in deep geothermal wells. The comparative analysis, along with the results from both models, were validated using geothermal well downhole data. Five distinct zones were selected for analysis, and the stick-slip severity value (SSV) was calculated using these two models (VDF and SDDF). The results from these two models for the five different zones were compared with the field data. The results indicated that VDF demonstrated superior quality when compared with field values, as the results of VDF were within the interquartile range of the observed SSV in each zone. A sensitivity analysis employing spider plots was performed for both models, considering parameters related to rock, bit, operational, and frictional aspects. In terms of the operational parameters, the weight-on-bit (WOB) and revolutions per minute (RPM) exerted the most significant influence on the SSV for both models. For the VDF model, the sensitivity analysis indicated that the frictional parameter, uniaxial compressive strength (UCS), and number of cutters (NOC) had the most pronounced impact on the SSV. In the case of the SDDF, the Intrinsic specific energy (ISE), bit diameter, and number of blades (NOB) are the key factors that predominantly affect the SSV.

Abstract The global consumption of electrical energy is constantly increasing, and the demand for sustainable and net-zero energy is more critical now than ever. In the context of the energy transition, Enhanced Geothermal Systems (EGS) are emerging as a promising solution for renewable and sustainable energy production. The economic efficiency of geothermal wells depends on production performance, which can be enhanced through various artificial lifting systems such as gas lift and submersible pumps. This paper investigates the application of gas lift in liquid-dominated geothermal wells to increase the production rate of hot liquid/steam, ultimately improving the enthalpy recovery per well. A steady-state multiphase flow model is employed to simulate the impact of gas lift on the flow rate within the geothermal well. The model considers various parameters such as fluid properties, wellbore geometry, and operational conditions. Sensitivity analysis is conducted to assess the influence of different gases on gas lift performance under varying reservoir conditions. The thermal performance of the geothermal well during gas lift operations is analyzed. The model accounts for heat transfer mechanisms, including conduction, convection, and radiation, within the wellbore and the surrounding formation. Factors such as fluid temperature, flow rate, gas injection temperature, and thermal properties of the materials are incorporated into the model to accurately simulate thermal dynamics. Different gas compositions, including compressed air, methane, nitrogen, and carbon dioxide are analyzed for their suitability as gas lift agents. Application of the gas lift results in up to a 30% increase in liquid production rates. Methane exhibits superior performance due to its lower density, with an increase in production from 1350 STB/day to 4050 STB/day at a 1 mmscf/day gas injection rate. CO2 shows potential for Carbon Capture, Utilization, and Storage (CCUS) projects, though it accelerates the corrosion risks by up to eight times. The study also finds that deeper gas injection points enhance mass production rates by 13%, albeit with higher surface gas injection pressures required. Overall, methane showed the best performance in terms of increasing production rates, producing the highest temperatures and therefore, the highest produced heat. However, the flammability and environmental damage risks must be considered. Thermal modeling indicates that while higher gas-to-liquid ratios lead to increased temperature drop, the overall enthalpy production rises due to the elevated production rate. The combination of multiphase flow and thermal modeling, along with comprehensive sensitivity analyses, provides a robust framework for evaluating gas lift applications in liquid-dominated geothermal wells. These findings contribute to optimizing geothermal well performance, highlighting the potential of gas lift systems to play a significant role in the transition towards sustainable and renewable energy sources.

Abstract This paper presents an update on the project to repurpose depleted oil wells into geothermal wells and shares a comprehensive workflow for obtaining permitting in Oklahoma. The objective is to provide a structured approach that addresses regulatory aspects, facilitating the transformation of oil wells into efficient geothermal energy sources. The methodology involves assessing geological conditions, analyzing regulations, and developing tailored monitoring and operation plans. The results highlight Oklahoma's potential as a significant renewable energy source through geothermal means, particularly in the Arkoma Basin and Anadarko Basin. However, challenges exist, including the need for a clear regulatory framework for deep geothermal energy, hurdles in obtaining landowner agreements, and regulatory constraints on well selection for heat transfer fluid re-circulation. The study offers novel insights and practical guidelines for engineers and policymakers in geothermal energy development, contributing to the field of sustainable energy.

Real-time drilling analysis requires knowledge of lithology at the drill bit. However, logging-while-drilling (LWD) sensors in the bottom hole assembly (BHA) are usually positioned 2–50 m (7–164 ft) above the bit (called the sensor offset), leading to a delay in real-time drilling analysis. The current industry solution to overcome this delay involves stopping drilling to perform a bottoms-up circulation for cuttings evaluation—a process that is both time-consuming and costly. To address this issue, our study evaluates three methodologies for real-time lithology prediction at the bit using drilling and petrophysical parameters. The first method employs a petrophysical approach, which involves using bulk density and neutron porosity predicted at the bit. The second method combines unsupervised and supervised machine learning (ML) for prediction. The third method employs classification algorithms on manually labeled lithology data from mud log reports, a novel approach used in this work. Our results show varying degrees of success: the bulk density versus neutron porosity cross-plot method achieved an accuracy of 58% with blind-well test data; the ML approach improved accuracy to 66%; and the Random Forest (RF) classification with manual labeling significantly increased accuracy to 86%. This comparative analysis of three different methodologies for lithology prediction has not been previously explored in the literature. While clustering and classification methods have been regarded as the most effective, our study demonstrates that they do not always yield the best result. These findings demonstrate that ML models, particularly the manual labeling approach, substantially outperform the petrophysical method. This new algorithm, designed for real-time applications, uses selected input parameters to effectively minimize problems associated with the sensor offset of LWD tools. It rapidly adapts to changes, offering a quicker and more cost-effective interpretation of lithology. This eliminates the need for time-consuming bottoms-up circulation to evaluate cuttings. Ultimately, this approach enhances drilling efficiency and significantly improves the accuracy of lithology prediction, notably in identifying interbedded geological layers.

Geomechanical studies in carbonate rocks often require the use of log relations to obtain mechanical properties when laboratory measurements are not available. This study presents a new set of equations to predict the unconfined compressive strength (UCS) and Young's modulus (E) for three carbonate lithologies: limestone, dolomite, and chalk. The equations are developed based on more than 700 mechanical-physical tests of carbonate rocks across different geological settings and geographical locations. The obtained results confirmed that petrophysical properties are consistent in determining the carbonate mechanical properties. The relations are developed based on either a single parameter or multiple parameters where coefficient of determination was improved for the multiple parameter relations. Scattering in the prediction of UCS and E is expected due to the carbonate heterogeneity in mineralogy, porosity, fabric as well as testing conditions. Thus, the applicable range of each relation is investigated. To test whether improved fits are achieved in this study, the relations are compared with the literature, and they showed a higher coefficient of determination. The proposed relations can be generally used as a starting point for UCS and E estimate when carbonate mechanical properties from laboratory tests are not available.

To analyze drilling performance a combination of Logging While Drilling data (LWD) and surface drilling data is combined. However, distance between some of the sensors, and the bit is greater than 20-30m (66-98 ft). In this case, determination of the LWD data at the bit becomes essential. This paper aims to implement machine learning algorithms to predict LWD data at the bit. The results of the model can be used to perform real-time analysis that considers the alterations in petrophysical properties, lithologies and rock strengths while drilling, without the drawbacks of LWD sensor offset. The aim of the paper is to predict LWD data at the bit by evaluating which supervised machine learning algorithm to incorporate. For training and validation of the model, a dataset of high porosity formations from multiple wells located in the North Sea has been used. Dataset included gamma ray (GR) log data recorded near the bit and drilling parameters recorded at the bit. Multi-linear regression (MLR), K-nearest neighbor (KNN) regression, random forest (RF) regression and support vector machine (SVM) regression are used for model building. The most efficient model with the best coefficient of determination (R2) is selected. The prediction forecasting for the random forest regression model was better among all the previously discussed regression models. The R2 value for the random forest regression model 98% and the KNN regression model came in second with R2 value at 95%. The worst performing regression model was the multi-linear regression model. This machine learning approach to consider the LWD sensor offset can be useful in the determination of petrophysical properties at the bit and in the real-time drilling analysis.

Mitigation of greenhouse gas emissions is becoming a significant factor in all industries. Cement manufacturing is one of the industries responsible for greenhouse gas emissions, specifically carbon dioxide emissions. Pozzolanic materials have long been used as cement additives due to the pozzolanic reaction that occurs when hydrated and the formation a cementitious material similar to that of cement. In this study, shale, which is a common component found in wellbore drill cuttings, was used in various sizes and quantities to determine the effect it had on the mechanical properties of wellbore cement. The unconfined compressive strength of the cement containing shale was compared to the cement without shale to observe the effect that both the quantity and particle size had on this property. SEM–EDS microscopy was also performed to understand any notable variations in the cement microstructure or composition. The samples containing micron shale appeared to have the best results of all the samples containing shale, and some of the samples had a higher UCS than one or more of the base case samples. Utilization of cuttings as a cement additive is not just beneficial in that it minimizes the need for cuttings removal and recycling, but also in that it reduces the amount of greenhouse gas emissions associated with cement manufacturing.

The Zubair formation is a significant producing reservoir in southern Iraqi oil fields. During the drilling of this formation, many wellbore instability issues were encountered, including shale caving, tight hole, lost circulation, and pipe sticking. These challenges significantly lead to an increase in non-productive time (NPT). The drilling data analysis indicated that the wellbore shear failure was the principal cause of these issues. The main objective of this study is to build a one-dimensional geomechanical model using core and well logging data from offset wells. Thus, the safe mud weight necessary to prevent the initiation of the shear and tensile failures against the Zubair formation was determined by employing the Mogi-Coulomb failure criterion. The results revealed that the horizontal and strongly deviated wells are less secure and stable than vertical and slightly deviated wells (less than 30°). This is based on a sensitivity analysis of the wellbore at a specific depth. The recommended mud weight for drilling wells having angles of inclination that vary from 0 to 30° is 11.9 to 12.3 ppg. A 140° NW-SE azimuth is the optimum azimuth to drill the deviated and horizontal wells which is analogous to the orientation of the minimal horizontal stress. The research’s findings can be applied to planning upcoming wells in the study region’s vicinity.

Wellbore leakage is a concern for abandoned oil and gas wells due to greenhouse gas emissions. The leakage mechanisms and resulting integrity are not well understood. Therefore, researchers have used analytical and numerical models to investigate wellbore integrity. An analytical solution, a finite element model without failure mechanisms, and a finite element model with failure criteria were developed and compared. The benefits and shortcomings of each model were discussed, and the different models were compared with three case study wells. The results of this work show that all three numerical models predict debonding between the cement sheath and the casing. However, including the failure criteria in the models proved to be critical in predicting correct stress distributions.

Drilling deep geothermal wells has proven to be a challenging endeavor, primarily due to issues such as loss circulation events, material limitations under high temperatures, and the production of corrosive fluids. Furthermore, the substantial upfront costs, coupled with geological and technical obstacles associated with drilling super-hot EGS wells in igneous rocks, hinder the widespread implementation of geothermal systems. Alternatively, geothermal energy development in sedimentary basins presents an opportunity for clean energy production with relatively lower investment costs compared to the development of super-hot EGS in igneous rocks. Sedimentary basins exhibit attractive temperatures for geothermal applications, and their wide distribution enhances the potential for nationwide deployment. Decades of drilling and development experience in oil and gas wells have yielded a wealth of data, knowledge, and expertise. Leveraging this experience and data for geothermal drilling can significantly reduce costs associated with subsurface data gathering, well drilling, and completion. This paper explores the economic viability of geothermal energy production systems in sedimentary basins. The study encompasses an analysis of time-to-hit-temperature (THT) and cost-to-hit-temperature (CHT) parameters, as well as Favorability maps across the United States. These maps are based on factors such as well depth, total drilling time, well cost, and subsurface temperature data. By integrating sedimentary basin maps and underground temperature maps, the THT and CHT maps can facilitate the strategic placement of EGS wells and other geothermal system applications in the most favorable locations across the United States.

Geothermal energy has vast potential as a reliable energy source of the future. However, its development has mostly been tied to specific geological locations or igneous rocks. Even though most western US regions have high thermal gradients compared to other places, higher temperatures are easily achievable by increasing the total depth in sedimentary rocks. The oil and gas industry has successfully mastered drilling sedimentary basins cost-effectively. Comparing cost/ft from typical sedimentary basins to granite or igneous rocks shows a tremendous difference. In addition, recent hydraulic fracturing technology transfer from the oil and gas industry can be deployed for geothermal applications. A potential new path toward expanded geothermal energy production is to use known porous and permeable reservoir rocks in appropriate sedimentary basins, where those formations have a sufficient temperature, thickness, porosity, and permeability, existing at depths that drilling time makes well construction costs economical for geothermal applications. In this paper, we will examine the unique potentials that sedimentary basins in Oklahoma offer to the geothermal industry for different end-user purposes, such as electricity generation or direct heat applications. The state has high geothermal gradients in some regions in the Arkoma Anadarko Basins that could be used for medium-temperature resources. Case studies from Oklahoma show how the many oil and gas wells in the state can enable geothermal direct-use projects. A state-wide levelized cost of energy analysis using geothermal gradient data indicates that there are areas with the potential to produce geothermal power at 14 cents/kWh or less. Geothermal energy has the potential to play a crucial role in Oklahoma's energy supply by offering a clean and renewable source of power that can fulfill energy demands.

Geothermal energy has the potential to be a dependable source of power in the future. However, its development has mostly been limited to specific geographical areas or types of rocks. The western US has relatively high downhole temperatures compared to other regions, however, similar temperatures could be found in other regions by drilling deeper into sedimentary rocks. The oil and gas industry has developed highly efficient and cost-effective methods for drilling in sedimentary basins. The main challenge is adapting these wells for geothermal energy production. When comparing the cost per foot of drilling in typical sedimentary basins to drilling in granite or igneous rocks, there is a significant cost saving for the geothermal industry. Furthermore, techniques for hydraulic fracturing used in the oil and gas industry can also be applied to geothermal energy production. A prospective way to increase the production of geothermal energy is to utilize known reservoir rocks with storage and flow capacity that allows water or steam cycling in sedimentary basins. These rocks have the appropriate temperature, thickness, porosity, and permeability and are located at depths that do not make the drilling costs too high for the system to be economically viable. This study will explore the unique advantages that Texas's sedimentary basins can bring to the geothermal industry, including electricity generation and direct heat utilization. Some regions of the Texas Gulf Coast have medium-high geothermal gradients, providing the potential for geothermal energy development. Texas's numerous oil and gas wells can support geothermal direct-use projects, as demonstrated by case studies. An analysis of the levelized cost of energy using geothermal gradient data suggests that there are areas with the potential to produce geothermal power at a cost of 14 cents or less per kWh. Geothermal energy has the potential to significantly contribute to Texas's energy supply by providing a clean and renewable source of power to meet energy needs.

This article presents a fiber optic rotary encoder-based pressure sensor for downhole applications. The rotary encoder, which is composed of an encoding pad and an all-glass optical fiber sensing head, converts the rotation angles of a pressure transducer to digital codes. The optical fiber only serves as the telemetry channel to directly transmit the data in digital format such that the environmental temperature influence and the long-time drift are minimized. The glass additive and subtractive manufacturing (ASM) techniques are used to embed the multichannel optical fibers into a bulk-fused silica glass substrate. Because the fused silica glass shares a similar material property to the optical fiber (e.g., thermal expansion coefficient), the optical fiber sensing head is well sealed and fixed even at an elevated temperature. The picosecond laser blackening technique is used to fabricate the high reflectivity contrast encoding pad with high accuracy. The proposed pressure sensor was manufactured and experimentally verified to have a signal-to-noise ratio (SNR) of 23 dB and a linear pressure response with a root-mean-square error (RMSE) of 0.37. During the 400-h 250 °C long-term stability test, there was no digital code variation. In addition, a mathematical model to study the relationships between the sensor’s performances and design parameters was established. According to the model, a 12-bit encoder (0.02% pressure sensitivity) can be achieved with a radius of 20 mm.