bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of Machine Learning Based Propped Fracture Conductivity Correlations in Shale Formations
Authors: Desouky Mahmoud , Tariq Zeeshan , Al jawad Murtada , Alhoori Hamed , Mahmoud Mohamed , Abdulraheem Abdulazeez 
Published: December 2021

Abstract
Propped hydraulic fracturing is a stimulation technique used in tight formations to create conductive fractures. To predict the fractured well productivity, the conductivity of those propped fractures should be estimated. It is common to measure the conductivity of propped fractures in the laboratory under controlled conditions. Nonetheless, it is costly and time-consuming which encouraged developing many empirical and analytical propped fracture conductivity models. Previous empirical models, however, were based on limited datasets producing questionable correlations. We propose herein new empirical models based on an extensive data set utilizing machine learning (ML) methods.
In this study, an artificial neural network (ANN) was utilized. A dataset comprised of 351 data points of propped hydraulic fracture experiments on different shale types with different mineralogy under various confining stresses was collected and studied. Several statistical and data science approaches such as box and whisker plots, correlation crossplots, and Z-score techniques were used to remove the outliers and extreme data points. The performance of the developed model was evaluated using powerful metrics such as correlation coefficient and root mean squared error.
After several executions and function evaluations, an ANN was found to be the best technique to predict propped fracture conductivity for different mineralogy. The proposed ANN models resulted in less than 7% error between actual and predicted values. In this study, in addition to the development of an optimized ANN model, explicit empirical correlations are also extracted from the weights and biases of the fine-tuned model. The proposed model of propped fracture conductivity was then compared with the commonly available correlations. The results revealed that the proposed mineralogy based propped fracture conductivity models made the predictions with a high correlation coefficient of 94%.
This work clearly shows the potential of computer-based ML techniques in the determination of mineralogy based propped fracture conductivity. The proposed empirical correlation can be implemented without requiring any ML-based software.
Single Well Microseismic Monitoring Leveraging Hybrid Cable Combining Both DAS and Traditional Geophones
Authors: Mizuno Takashi , Le Calvez Joel , Cuny Theo , Chen Yu 
Published: December 2021

Abstract
The single monitoring well configuration is a favorable option for microseismic monitoring considering risk and cost. It has commonly been used in various industries for decades. When using a single monitoring well, we rely among other things on the waveforms’ polarization information to accurately locate detected microseismic events. Additionally, using a large array aperture reduces hypocenter's uncertainty.
Instead of solely relying on 3C geophones to achieve such objectives, we propose to combine 3C sensors and distributed acoustic sensing (DAS) equipment. It is quite a cost-effective solution, and it enables us to leverage each system's strength while minimizing their respective limitations when considered individually. We present the technical feasibility of such a hybrid microseismic monitoring system using data acquired during a monitoring campaign performed in the Montney formation, Canada. In this dataset, the optic fiber (DAS) is located in the wireline cable used to deploy the 3C geophones; themselves located at the bottom of the DAS wireline cable. Though different acquisition systems are employed for the geophone array and the DAS array, both datasets are GPS time stamped so that data can be processed properly. We scan the DAS data using an STA/LTA event detection, and we integrate with the 3C geophone data. We find the microseismic waveform in both the DAS and the geophone sections and confirm the arrival times are consistent between DAS and geophones. Once datasets are merged, we determine hypocenters using a migration-based event location method for such hybrid array. The uncertainty associated with the event located using the hybrid DAS – geophone array is smaller than for any of the systems looked at independently thanks to the increased array aperture. This case study demonstrates the viability and efficiency of the next generation of a single well acquisition system for microseismic monitoring. Not only does it lower event location uncertainty, but it is also more reliable and cost-effective than the conventional approaches.
Artificial Intelligence and Robotics in the Oil Industry: Will it Take My Job?
Authors: Agbaji Armstrong Lee 
Published: December 2021

Abstract
Historically, the oil and gas industry has been slow and extremely cautious to adopt emerging technologies. But in the Age of Artificial Intelligence (AI), the industry has broken from tradition. It has not only embraced AI; it is leading the pack. AI has not only changed what it now means to work in the oil industry, it has changed how companies create, capture, and deliver value. Thanks, or no thanks to automation, traditional oil industry skills and talents are now being threatened, and in most cases, rendered obsolete. Oil and gas industry day-to-day work is progressively gravitating towards software and algorithms, and today’s workers are resigning themselves to the fact that computers and robots will one day "take over" and do much of their work. The adoption of AI and how it might affect career prospects is currently causing a lot of anxiety among industry professionals.
This paper details how artificial intelligence, automation, and robotics has redefined what it now means to work in the oil industry, as well as the new challenges and responsibilities that the AI revolution presents. It takes a deep-dive into human-robot interaction, and underscores what AI can, and cannot do. It also identifies several traditional oilfield positions that have become endangered by automation, addresses the premonitions of professionals in these endangered roles, and lays out a roadmap on how to survive and thrive in a digitally transformed world.
The future of work is evolving, and new technologies are changing how talent is acquired, developed, and retained. That robots will someday "take our jobs" is not an impossible possibility. It is more of a reality than an exaggeration. Automation in the oil industry has achieved outcomes that go beyond human capabilities. In fact, the odds are overwhelming that AI that functions at a comparable level to humans will soon become ubiquitous in the industry. The big question is: How long will it take?
The oil industry of the future will not need large office complexes or a large workforce. Most of the work will be automated. Drilling rigs, production platforms, refineries, and petrochemical plants will not go away, but how work is done at these locations will be totally different. While the industry will never entirely lose its human touch, AI will be the foundation of the workforce of the future. How we react to the AI revolution today will shape the industry for generations to come. What should we do when AI changes our job functions and workforce? Should we be training AI, or should we be training humans?
The Effects of Interfacial Tension Dependent Relative Permeability on Sweep Efficiency and Oil Recovery Under Different Gas Injection Composition
Authors: Abd Karim Mohd Ghazali , Hidayat Wahyu , Abdulelah Alzahrani 
Published: December 2021

Abstract
The objective of this paper is to investigate the effects of interfacial tension dependent relative permeability (Kr_IFT) on oil displacement and recovery under different gas injection compositions utilizing a compositional simulation model. Oil production under miscible gas injection will result in variations of interfacial tension (IFT) due to changes in oil and gas compositions and other reservoir properties, such as pressure and temperature. Laboratory experiments show that changes in IFT will affect the two-phase relative permeability curve (Kr), especially for oil-gas system. Using a single relative permeability curve during the process from immiscible to miscible conditions will result in inaccurate gas mobility against water, which may lead to poor estimation of sweep efficiency and oil recovery. A synthetic sector compositional model was built to evaluate the effects of this phenomenon. Several simulation cases were investigated over different gas injection compositions (lean, rich and CO2), fluid properties and reservoir characterizations to demonstrate the impact of these parameters. Simulation model results show that the application of Kr_IFT on gas injection simulation modelling has captured different displacement behavior to provide better estimation of oil recovery and identify any upside potential.
Multi-Fidelity Bayesian Approach for History Matching in Reservoir Simulation
Authors: Santoso Ryan , He Xupeng , Alsinan Marwa , Figueroa Hernandez Ruben , Kwak Hyung , Hoteit Hussein 
Published: December 2021

Abstract
History matching is a critical step within the reservoir management process to synchronize the simulation model with the production data. The history-matched model can be used for planning optimum field development and performing optimization and uncertainty quantifications. We present a novel history matching workflow based on a Bayesian framework that accommodates subsurface uncertainties. Our workflow involves three different model resolutions within the Bayesian framework: 1) a coarse low-fidelity model to update the prior range, 2) a fine low-fidelity model to represent the high-fidelity model, and 3) a high-fidelity model to re-construct the real response. The low-fidelity model is constructed by a multivariate polynomial function, while the high-fidelity model is based on the reservoir simulation model. We firstly develop a coarse low-fidelity model using a two-level Design of Experiment (DoE), which aims to provide a better prior. We secondly use Latin Hypercube Sampling (LHS) to construct the fine low-fidelity model to be deployed in the Bayesian runs, where we use the Metropolis-Hastings algorithm. Finally, the posterior is fed into the high-fidelity model to evaluate the matching quality. This work demonstrates the importance of including uncertainties in history matching. Bayesian provides a robust framework to allow uncertainty quantification within the reservoir history matching. Under uniform prior, the convergence of the Bayesian is very sensitive to the parameter ranges. When the solution is far from the mean of the parameter ranges, the Bayesian introduces bios and deviates from the observed data. Our results show that updating the prior from the coarse low-fidelity model accelerates the Bayesian convergence and improves the matching convergence. Bayesian requires a huge number of runs to produce an accurate posterior. Running the high-fidelity model multiple times is expensive. Our workflow tackles this problem by deploying a fine low-fidelity model to represent the high-fidelity model in the main runs. This fine low-fidelity model is fast to run, while it honors the physics and accuracy of the high-fidelity model. We also use ANOVA sensitivity analysis to measure the importance of each parameter. The ranking gives awareness to the significant ones that may contribute to the matching accuracy. We demonstrate our workflow for a geothermal reservoir with static and operational uncertainties. Our workflow produces accurate matching of thermal recovery factor and produced-enthalpy rate with physically-consistent posteriors. We present a novel workflow to account for uncertainty in reservoir history matching involving multi-resolution interaction. The proposed method is generic and can be readily applied within existing history-matching workflows in reservoir simulation.
Laser Technology for Downhole Applications; Past, Present and Future
Authors: Batarseh Sameeh , Mutairi Saad , San Roman Alerigi Damian , Al Harith Abdullah 
Published: December 2021

Abstract
The objective of this work is to provide an overview of high power laser program since it is inception and to provide the strategy to make it reality. An overview of the past two decades, current and future plan to deploy the technology in the field.
Laser attracted the oil and gas industry as an innovative non-damaging technology and alternatives to current practices. The lab success conducted over the past 20 years performing experiments on thousands of representative rock samples proved the key parameter for successful laser operation in the field. The technology is not only a non-damaging but also improves flow properties and communication between the wellbore and the hydrocarbon bearing formation.
For the past two decades, researchers attempted to deploy high power laser technology for several downhole applications due to its unique properties such as accuracy, precision, and power. The power of the earlier laser generation was insufficient to penetrate subsurface formations. Recent advancement in the high power laser technology generates new and evolved systems that are more compact, efficient, and cost effective for downhole applications.
Thousands of rocks have been exposed to high power lasers radiations for several downhole applications such as perforation, drilling and heating. The success of the technology demonstrated that in all rock types, the flow properties were enhanced regardless of their compressive strength and hardness. Laser also has unique futures such as the precision in controlling and orienting the energy in any direction regardless of the reservoir stress orientation and magnitude. The beam is generated at the surface and delivered downhole via fiber optics cable, it can be targeted directly to the pay zone to enable production from challenging zones that cannot and could not be achieved with current technology. The technology provides small footprint and environmentally friendly technology, it provides waterless technology as an alternative to water base fracturing technology.
Managing Sanding Risk in Sandstone Reservoir Through a New Constitutive Model
Authors: Subbiah Surej Kumar , Samsuri Ariffin , Mohamad-Hussein Assef , Jaafar Mohd Zaidi , Chen Yingru , Pearce Andrew , Ranjan Kumar Rajeev , Paramanathan Rajendra Nath , de Groot Lex 
Published: December 2021

Abstract
Sandstone reservoir failure during hydrocarbon production can cause negative impact on the oil/gas field development economics. Loss of integrity and hydrocarbon leakage due to downhole or surface erosion can decrease the risk of operational safety. Therefore, a proper understanding of the best formulation to manage and find the balance between productivity and sand risk is very important. Making decisions for the best and most economical completion design needs a full and proper sanding risk analysis driven by geomechanics modeling. The accuracy of modeling the reservoir rock mechanical behavior and the failure analysis depends on the selection of the constitutive model (failure criteria) specially to understand the failure and post failure mechanisms. Thus, an appropriate constitutive model/criterion is required as most of the current model/criteria are not developed for a weak rock material honoring the non-linearity and post failure (softening) process. Therefore, a new and novel elasto-plastic constitutive model for sandstone rock has been investigated and developed. The effort started with a sequence of triaxial tests at different confining pressures on core samples. Different types of rock have been tested during the developing and validation of the constitutive model. Comparison with other existing failure criteria was also performed. As the results, the newly developed constitutive model is better honoring the full spectrum of elasto-plastic rock mechanical behavior (softening and post-failure) which is important for oil and gas applications, specifically for sand production and drilling i.e. failure stabilization due to stress relief. The formulation and process are demonstrated with a case study for an old gas field, where a few gas wells have been shut-in due to severe sand production. The sand production predictive models have been validated with downhole pressure. The wells have been side-tracked and recompleted using the new sand failure prediction, using the new formulation resulted in restoring sand-free production at former rates. The novelty of this study would be in finding the right formula to best design the predictive model and to avoid any sand production when using the newly developed constitutive model.
An Experimental and Simulation Study of CO2 Sequestration in an Underground Formations; Impact on Geomechanical and Petrophysical Properties
Authors: Fatima Sobia , Khan Hafiz Muhammad Mutahhar , Tariq Zeeshan , Abdalla Mohammad , Mahmoud Mohamed 
Published: December 2021

Abstract
Carbon dioxide (CO2) sequestration is a technique to store CO2 into an underground formation. CO2 can cause a severe reaction with the underground formation and injection tubing inside the well. Successful CO2 storage into underground formations depends on many factors such as efficient sealing, no escaping from the storage, and minimum corrosion to injection tubing/casing. Therefore, proper planning involving thorough study and reaction kinetics of CO2 with the underground formation is indeed necessary for proper planning.
The main aim and objective of this study are to investigate the effect of CO2 storage with different cap rocks such as tight carbonate and shale under simulated reservoir conditions. The samples were stored for different times such as 10, 20, and 120 days. The objectives of the study were achieved by carrying out extensive laboratory experiments before and after sequestration. The laboratory experiments included were rock compressive and tensile strength tests, petrophysical tests, and rock mechanical tests. The laboratory results were later used to investigate the reaction kinetics study of CO2 with the underground formation using CMG simulation software. The effect of injection rate, the point of injection, purity of the injection fluid, reservoir heterogeneity, reservoir depth, and minimum miscibility pressure was analyzed.
In this simulation model, CO2 is injected for 25 years using CMG-GEM simulation software and then the fate of CO2 post injection is modeled for the next 225 years. The simulation results showed a notable effect on the mechanical strength and petrophysical parameters of the rock after sequestration, also the solubility of CO2 decreases with the increase in salinity and injection pressure. The results also showed that the storage of CO2 increases the petrophysical properties of porosity and permeability of the formation rock when the storage period is more than 20 days because of calcite precipitation and CO2 dissolution. A storage period of fewer than 20 days does not show any significant effect on the porosity and permeability of carbonate reservoir rock. A sensitivity analysis was carried out which showed that the rate of CO2 sequestration is sensitive to the mineral-water reaction kinetic constants. The sensitivity of CO2 sequestration to the rate constants decreases in magnitude respectively for different clay minerals.
The new simulation model considers the effect of reaction kinetics and geomechanical parameters. The new model is capable of predicting the compatibility of CO2 sequestration for a particular field for a particular time.
Utilizing Microseismic to Monitor Fracture Geometry in a Horizontal Well in a Tight Sandstone Formation
Authors: Sani Abu M. , AlQasim Hatim S. , Alidi Rayan A. 
Published: December 2021

Abstract
This paper presents the use of real-time microseismic (MS) monitoring to understand hydraulic fracturing of a horizontal well drilled in the minimum stress direction within a high-temperature high-pressure (HTHP) tight sandstone formation. The well achieved a reservoir contact of more than 3,500 ft. Careful planning of the monitoring well and treatment well setup enabled capture of high quality MS events resulting in useful information on the regional maximum horizontal stress and offers an understanding of the fracture geometry with respect to clusters and stage spacing in relation to fracture propagation and growth. The maximum horizontal stress based on MS events was found to be different from the expected value with fracture azimuth off by more than 25 degree among the stages. Transverse fracture propagation was observed with overlapping MS events across stages. Upward fracture height growth was dominant in tighter stages. MS fracture length and height in excess of 500 ft and 100 ft, respectively, were created for most of the stages resulting in stimulated volumes that are high. Bigger fracture jobs yielded longer fracture length and were more confined in height growth. MS events fracture lengths and heights were found to be on average 1.36 and 1.30 times, respectively, to those of pressure-match.
Experimental Study and Prediction Model for Hydrate Plugging Formation in Single-Pass Gas-Dominated Pipes with Diameter Reduction
Authors: Zhang Jianbo , Wang Zhiyuan , Chen Shengnan , Pei Jihao , Ma Nan , Tong Shikun , Lou Wenqiang , Sun Baojiang 
Published: December 2021

Summary
Gas hydrate plugging in flowlines acts as a major blockage risk in oil, gas, and natural gas hydrate production. Current studies on hydrate plugging is mainly conducted in pipes with a constant diameter, whereas the effects of varying diameters have been less explored. Pipes with diameter reductions are very common in the oil and gas production process. Herein, by performing experiments with pipes of four different sizes, including one with a constant diameter and three with diameter reductions, the hydrate plugging in single-pass gas-dominated pipes with diameter reduction is investigated systematically, the results of which show that the existence of diameter reduction can facilitate the deposition of hydrate particles on pipe walls and the formation of a hydrate deposition layer. Meanwhile, hydrate sloughing occurs during the growth of the hydrate deposition layer under fluid shear force. With the increase in the diameter reduction ratio or subcooling for hydrate formation, the hydrate deposition is observed to increase significantly, thus resulting in the reduction of time for hydrate plugging. According to these results, the hydrate plugging mechanism in pipes with diameter reduction is proposed. Further, in combination with the hydrate deposition difference between the inside and outside of the arc-shaped low-speed area, a new numerical model is established for hydrate plugging prediction in the pipe with diameter reduction, which can predict the growth and evolution of the hydrate deposition layer accurately with the consideration of the diameter’s reduction. The results of this work provide a valuable guidance for the targeted prevention and management of hydrate plugging in flowlines with diameter reductions.