bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing Pressure Transient Analysis with Automatic Model Identification: A Machine Learning Approach
Authors: Nukala S. T. , Kumar A. , Rajput S. , Lopes V. S. , Wydiabhakti T. B. 
Published: April 2024

Abstract
Over its historical trajectory, Pressure Transient Analysis (PTA) has experienced a notable evolution, commencing with early techniques like straight-line analysis and type curve matching. The mid-20th century witnessed the introduction of pressure derivatives, enhancing the sophistication of reservoir behavior interpretation. Deconvolution methods took center stage in the late 20th century, offering intricate insights into wellbore and reservoir responses. In the contemporary era of PTA, log-log analysis has become the norm, featuring the plotting of pressure and its derivatives on logarithmic scales. Recent strides in the field concentrate on the integration of automation and machine learning to expedite PTA processes.
Our methodology enhances the Pressure Transient Analysis (PTA) process by leveraging a framework based on triplet loss. This architecture seamlessly integrates Convolutional Neural Network (CNN) layers, providing robust feature extraction capabilities for the automated analysis of pressure transient data. The model is trained using simulated experimental data generated through a systematic Design of Experiments (DOE) approach. This involves incorporating the ten most prevalent interpretation scenarios, encompassing well, reservoir, and boundary model types. For each model type, critical parameters such as permeability, horizontal well length, skin factor, and distance to the boundary are systematically sampled, resulting in the computation of 800 distinct pressure derivative responses.
The triplet loss framework adopts a self-supervised training strategy, where anchor, positive and negative pairs are dynamically generated from the simulated training dataset. The loss function encourages the network to reduce the distance between the anchor and positive examples while increasing the distance between the anchor and negative examples.
The experimental analysis revealed that the actual model class consistently ranked high among the top classes. The model exhibits an accuracy rate of 90% in providing the top-ranked model recommendations when evaluated on 100 samples derived from the 8 interpretation scenarios. Having prior knowledge about the most probable well test models at the top ranks diminishes the manual effort required for analysis.
This approach can expedite the identification of the pressure derivative response associated with specific combinations of well, reservoir, and boundary models, leading to the generation of models with reduced reliance on user interaction. The methodology streamlines the recognition of models for interpretation engineers, enabling faster integration with additional information from diverse sources like geophysics, geology, petrophysics, drilling, and production logging.
A Data-Driven Journey into Liquid Loading Detection and Prediction
Authors: Sinha Utkarsh , Chauhan Prithvi Singh , Zalavadia Hardikkumar , Adil Arsalan , Sankaran Sathish 
Published: April 2024

Abstract
Liquid loading is a persistent challenge encountered in onshore and offshore gas wells, particularly at low gas rates, where the accumulation of wellbore liquids leads to flow instabilities, operational disruptions, prolonged shut-ins leading to early well abandonment and reduced overall recovery. Typical liquid loading mitigation strategies such as velocity strings, well cycling, intermittent or permanent artificial lift, surfactants/soaps etc. involve additional cost and therefore, their operational success relies on selecting the right wells with proper understanding of expected critical rates at the right time. Models based on droplet, film or multiphase transient flow can be used to model liquid loading phenomenon. However, empirical correlations are used commonly to detect liquid loading, which often lack precision in field applications due to oversimplified assumptions regarding liquid behavior and flow regime consistency.
In this work, we introduce an innovative data-driven approach for liquid loading detection and prediction (LLDP) that harnesses high-frequency gas rate and tubing head pressure measurements to identify the onset of liquid loading and use it to correct critical rates computed by empirical methods. The LLDP methodology involves the computation of diagnostic statistical proxy features, enabling the characterization of flow instability arising from liquid loading. Upon detection, subsequent adjustment to gas rates via feedback control facilitate the determination of corrected critical rate by calibration of empirical correlations such as Turner and Coleman. This also allows us to predict when the reservoir energy will no longer be sufficient to lift the liquids combined with the corrected critical gas curve and estimate time to liquid loading.
Through extensive application across various unconventional and conventional gas fields, the LLDP method has consistently matched detected liquid loading events with manual field observations, eliminating biases and subjective interpretations. In a specific case study, the utilization of adjusted critical rates to optimize gas lift operations resulted in substantial cost savings by minimizing the need for buy-back gas. In another example, the detection of liquid loading and prediction helped optimize onshore gas well operations and field management practices.
This presentation highlights a rapid and systematic approach for the detection, quantification, and prediction of liquid loading, utilizing readily available field data without relying on assumptions. By accurately identifying critical gas rates and optimizing artificial lift configurations, the LLDP method offers a practical solution to the challenges posed by liquid loading in gas wells, ultimately enhancing operational efficiency and maximizing production.
A Real Time Cloud-Based Transient Modeling of Wellbore Fluids Enabling Safer Deep Transient Testing
Authors: Lopes Velerian Sanjao , Kumar Arvind , Nukala Poorna Venkata Sai Teja , Wydiabhakti Tety Benedicta 
Published: April 2024

Abstract
Wellbore dynamics is one of the key factors in reservoir testing, acting as a bridge between the reservoir and surface measurements. The objective of this research is to address the challenges encountered in well control and highlight the outcomes of employing wellbore dynamic simulation to enhance the safety of formation tester (FT) sampling and deep transient tests (DTT) conducted in wells, with a special emphasis on pre-job simulations multi-parameter sensitivity analysis. This effort is geared towards advancing our comprehension of the interaction between hydrocarbons and wellbore mud during and following FT pump-out operations. The most recent advancement in DTT technology allows for the pumping of a larger volume of hydrocarbons into the wellbore, when compared to the operation of a conventional formation tester. While conducting DTT, formation fluids pumped from the well are mixed with drilling mud from the surface through a circulation sub into the annulus. This mixture of fluids is then circulated out from the annulus to the surface during the flowing period. It is imperative to possess a thorough comprehension of these procedures to ensure well control safety. Consequently, the utilization of a dynamic multiphase flow simulator that considers the interactions between downhole pumped hydrocarbons and drilling fluids becomes crucial to enhance the accuracy of pressure simulations during the DTT operation. Given the paramount importance of safety in oil and gas operations, a cloud-based wellbore dynamics simulator enables precise quantification of drilling fluid adjustments, circulation rates, hydrocarbon composition, downhole pump rates, well depth, hole diameter, overbalance pressure, and pump duration for various FT design sequences. This allows for accurate forecasting of downhole well pressure and the distribution of free gas throughout the well, adjusting these parameters as needed. Subsequently, we will explore scenarios with kick potential and risk mitigation strategies. This paper showcases a total of 15 case studies (different hydrocarbon types and overbalance scenarios), where cloud-based wellbore fluid simulations were performed for different flow rate scenarios, and to predict the potential well control situations. A special emphasis was given to the near critical hydrocarbon fluids such as condensate, volatile oil, and wet gas.
Efficient and Sustainable Scale Removal: Unveiling the Power of Pressure Pulsing in Restoring Wellbore Accessibility
Authors: Aljauydi H. S. , Mehmood S. X. , Aljuaid B. M. , Abouakar A. H. 
Published: April 2024

Abstract
This study aims to investigate the safe and environmentally friendly application of pressure pulsing technique for effectively removing calcium sulphate scale (CSS) blockages in wellbores. The objective is to assess the ability of the pulsing tool to restore full wellbore accessibility while minimizing the carbon footprint.
The pressure pulsing tool utilized a combination of slickwater and gel to create a controlled water hammering effect, which facilitates the disruption of sand bridges, loosens particles, and clears blockages. Implemented with a four-port configuration, the tool expels fluids downward and outward, effectively eroding the CSS blockage through cavitation erosion. This process ensures a clean and accessible wellbore while promoting safety and reducing the carbon footprint.
The pressure pulsing technique successfully eliminated 41 feet of wellbore blockage, corresponding to the removal of 140 kg of accumulated CSS. This mechanical action, driven by cavitational erosion, proved crucial in the cleaning process. Further analysis revealed that the pulsing tool's optimized operational parameters led to increased penetration rates and improved removal efficiency. By employing the pressure pulsing technique, the study provides a safe, effective, and environmentally conscious solution for addressing CSS blockages, thereby enhancing well productivity and reducing the carbon footprint.
This study presents a pioneering approach using pressure pulsing technique to safely remove and clean CSS blockages in wellbores. By prioritizing safety and environmental consciousness, the application of this technique contributes to the reduction of potential hazards and the overall carbon footprint in the oil and gas industry. The findings provide valuable insights into improving operational practices and demonstrating the effectiveness of pressure pulsing as an eco-friendly solution for maintaining wellbore accessibility.
Carbon Sequestration in Mid Ocean Ridge Hydrothermal Systems
Authors: Croft Gregory D. 
Published: April 2024

Abstract
The objective of this paper is to explore a novel environment for carbon sequestration. Mid ocean ridge hydrothermal systems flow enormous volumes of water through vesicular porosity and natural fracture systems in basalt. Since the basic premise of carbon mineralization projects is to dissolve carbon dioxide in water and react the resulting carbonate ions with minerals in mafic or ultramafic igneous rock, the presence of so much natural flow through such rocks presents an opportunity. The proposed concept is to inject carbon dioxide in or near natural seawater intakes associated with these hydrothermal systems. Snæbjörnsdóttir and Gislason (2016) assessed the carbon dioxide sequestration potential in basalt offshore Iceland and concluded that up to 7000 GtCO2 could be stored offshore Iceland within its exclusive economic zone, so volumes of basalt are not a limiting factor.
Formation Evaluation and Behind Casing Opportunity Analysis Using Multi-Output Regression and Machine Learning Techniques
Authors: Hassani H. , Shahbazi A. , Fadhli M. Z. , Hamdi Z. , Hassan A. M. , Masoudi R. , Bataee M. 
Published: April 2024

Abstract
Generally, Behind Casing Opportunity (BCO) provides a good alternative to maximize brownfield production by enabling access to the remaining oil targets behind existing completion. Proper BCO maturation which includes minor reservoir unit analysis may offer a high return, low-risk item in terms of obtaining cheap oil at low cost. Unfortunately, a lack of proper resources such as possible manpower and budget constraints may pose a challenge for proper BCO analysis. A novel application of multioutput regression Random Forest algorithm to predict both BCO and fluid code for BCO determination marks a good start for further implementation of machine learning to tackle this problem. The multi-output model enables two or more variables to be predicted hence allowing uniformed prediction rules and time-saving alternatives. Exploratory data analysis (EDA) and required data preprocessing were carried out to provide excellent inputs for the algorithm. The algorithm produced two outputs of predicted BCO and fluid code with root mean squared error (RMSE) of 0.0933 and R2 of 0.9619. To properly support the logic of the model prediction, the log curve of both predicted values was plotted and the rationale behind the predicted result was observed. Besides, a good cross-plot correlation of both the predicted and actual value of the outputs also aided in further validating the result. The research potentially can help to further enhance BCO analysis by giving robust and very effective methods for both BCO and fluid detection. Furthermore, predicting fluid code helps for proper reservoir analysis hence providing a time-saving alternative for better drilling decisions.
Improving Production and Hydrocarbon Recovery from Viscous Oil Reservoirs in Ecuador While Minimizing the Carbon Intensity of Production Using AICD Technology
Authors: Konopczynski Michael , Grefa Martina , Castillo Martha , Villavicencio Angel , Solano Maurilio , Chanaga Arlen 
Published: April 2024

Abstract
This paper describes an integrated workflow used to evaluate the potential improvement in oil production and hydrocarbon recovery with the implementation of AICD completions in these fields, the completion designs and implementation, and post installation well performance. This paper summarizes the technical learnings from this project. In addition, the paper describes the potential reduction in carbon intensity of the produced crude by significant reduction in the energy used to lift, process and dispose of produced water.
Optimizing Injection Well Trajectory to Maximize Storage Security and Minimize Geomechanical Risk
Authors: Li Y. , ONeal R. , Whitezell M. , Kovscek A. R. 
Published: April 2024

Summary
The objective is to demonstrate an optimal well design for a potential geological carbon storage (GCS) project. CO2 plume shape, size, and pressure response in the subsurface are design variables. The chosen well trajectory improves injectivity while minimizing formation pressure buildup. The CO2 plume shape and migration are controlled within a complex dipping storage formation. In order to achieve the goals, we designed a toolbox (pyCMG) to standardize the well design optimization process that is applicable to different carbon storage assets. This toolbox is helpful to maximize storage security and minimize geomechanical risk. We developed a numerical model of transport within a storage formation fully coupled with geomechanical deformation to represent a prospective GCS site in Kern County, California. It honors a pre-defined injection scheme with injection rates that ramp up and then decline for a total of 12.3 Mt of CO2 injection in 18 years. The peak injection rate is greater than 1 Mt/yr whereas the post injection period is 100 years. The pyCMG toolbox allows efficient computations for hundreds of cases. It is useful to understand potential outcomes and optimize the well trajectory to fulfill plume and pressure buildup constraints while satisfying the target inject amount.
We propose to develop a long, deviated injection well to best address the injectivity and plume migration challenges for this heterogeneous, dipping formation. The well design optimization successfully reduces the pressure build-up by 54% over the base design while only increasing the areal extent of the plume by 8.4%. We quantified the carbon dioxide plume shape and size at the land surface. The plume grows rapidly at the beginning due to injection, it increases slightly after shut-in due to slow up-dip migration driven by buoyancy, and becomes stationary within the post-injection monitoring period. The optimal injector design balances the optimization goals of CO2 plume size, pressure increase in storage formation, and pressure build-up at fault. The optimal well is robust under uncertainties from injection schemes and geological model realizations. The best injector is capable to enlarge the total storage amount with an average of annual injection rates greater than 1 Mt/yr. Rock deformation due to the pressure buildup is also computed. The maximum land uplift is predicted to be 2.1 cm during the year of the peak annual injection rate. Land surface uplift strongly correlates with the subsurface pressure response.
Predicting Future Heat Outputs from Enhanced Geothermal System Utilizing Machine Learning Approach
Authors: Nath Fatick , Romero Nora A Garcia , Cabezudo Eleazar , Sutradhar Shaon , Cisneros Erik , Portillo Orlando , Gindic Ronen 
Published: April 2024

Abstract
The Earth is a vast energy reservoir. The U.S. Department of Energy estimates that harnessing just 0.1% of the Earth's geothermal energy can power humanity for 2 million years. The energy sector has shown a significant interest in geothermal energy owing to its advancements in renewable energy, environmental friendliness, and widespread accessibility. An improved geothermal system (EGS) efficiently extracts heat from deep hot dry rock (HDR). However, EGS is battling to ensure safe drilling and appropriate fracturing to extract heat potential. Essential aspects to evaluate are deformation and fracture face damage during induced fracturing in order to extract heat energy from HDR, due to its heterogeneities. This study examines and predicts future heat outputs from EGS utilizing machine learning.
The UTAH FORGE well, 16B (78)-32, provided the well logs and petrophysical characteristics. The single-well data was divided into three categories: training, testing, and validation, with a 70:20:10 ratio. The model was built using eleven well-log variables in total, including anisotropy in heat, density, porosity, Poisson ratio, compressional and shear travel times, and SP and GR. Machine Learning model (ML), Decision Tree (DT) and Random Forest (RF) model were constructed, and an optimization technique was employed to ascertain the hyperparameters of the ideal model for heat production prediction.
The pair plot indicates that there is no discernible noise present in the recorded data, and the correlation matrix illustrates a perfect correlation (unity) between temperature and depth. The machine learning model exhibited outstanding performance in forecasting the future temperature of the geothermal reservoir. Both Random Forest (RF) and Decision Tree (DT) models displayed exceptional accuracy, achieving R2 scores exceeding 98% with RMSE values below 3%. Particularly, the Random Forest model surpassed traditional approaches, achieving an accuracy of approximately 99.7%. These results suggest that these models remain capable of generating reliable and useful projections.
Impact of Wettability and Relative Permeability Hysteresis in Saline Aquifers; Implication of Hydrogen Underground Storage
Authors: AL homoud Rana , Machado Marcos Vitor Barbosa , Daigle Hugh , Sepehrnoori Kamy , Ates Harun 
Published: April 2024

Abstract
Numerical simulation is a crucial step in evaluating hydrogen storage in porous media and plays a crucial role in complementing and extending the insights gained from traditional laboratory experiments. This study focuses on numerically evaluating the influence of wettability and relative permeability hysteresis on hydrogen recovery in underground hydrogen storage. Wettability and hysteresis play pivotal roles in determining trapped gas saturation and significantly affect hydrogen recovery. Neglecting hysteresis may lead to overestimating gas production and misrepresenting water production.
The investigation employs the Carlson model to calculate trapped gas saturation and the Killough model to account for the water hysteresis. By utilizing the Land coefficient based on laboratory-measured data for the hydrogen-brine system, our results reveal a substantial impact of gas hysteresis on the hydrogen recovery factor. The base model, neglecting the hysteresis effect, indicates a recovery factor of 78% by the fourth cycle. In contrast, the modified model, accounting for hysteresis and yielding a trapped gas saturation of ~17%, shows a hydrogen recovery factor of 45% by the fourth cycle. Furthermore, gas hysteresis notably impacts water production, with an observed 12.5% increase in volume in the model incorporating gas hysteresis. Additionally, results suggest that water hysteresis is significant in UHS, and a substantial reduction of hydrogen recovery and water production was observed. In conclusion, relative permeability hysteresis significantly influences hydrogen production compared to other petrophysical phenomena, such as wettability, which has a limited impact on operational feasibility and poses little threat to storing hydrogen in sandstone formations. In contrast to numerous numerical simulation studies that neglect hysteresis, this research offers a comprehensive analysis underscoring the significance of hysteresis on UHS. This contribution enhances the precision of recovery factor data estimation, which is crucial for assessing storage project feasibility.
Significance of ploidy on par with genotypic specificity in host resistance for Phytophthora in Black pepper (Piper nigrum L.)
Authors: Jacob Mathew Paravanparampil , Davis Thomson , Jabbar M. Abdul 
Published: April 2024

Abstract

We estimated Disease Severity Indices (DSI) of 22 genotypes of Black pepper by subjecting them to screening against Phytophthora capsici for assessing the influence of ploidy as well as genotype in host resistance. The DSI of the genotypes showed significant variation and established the existence of genotypic specificity in host resistance in Black pepper. ‘PAJ’, a haploid genotype of the crop, exhibited the highest resistance to the pathogen, as evident by its lowest DSI (33.33 %), and rated it as ‘moderately resistant’. Its diploid progenitor ‘PMM’ and all the other diploid genotypes were rated as ‘susceptible’, since their DSI were > 40 %. ‘PAJ’ is genetically a product of haplodisation of ‘PMM’, and it showed significantly higher resistance than ‘PMM’ for the pathogen. Hence, it is inferred that ploidy has remarkable influence in Black pepper – Phytophthorapathosystem. This study is insightful on the possible genomic interventions for augmenting the inherent degree of resistance exhibited by the genotypes of the crop for the pathogen. Therefore, outcome of this study is a breakthrough, which can transform the present-day haphazard, less efficient approach in resistance breeding in Black pepper for Phytophthora foot rot to one which is systematic and efficient.