bluuebunny/crossref_metadata_2025_split huggingface dataset:
Depletion Effect Quantification and Mitigation of the Offset Wells
Authors: Dashti Ghazal , Liang Baosheng 
Published: October 2020

Abstract
Depletion effects occurs in unconventional reservoirs when the hydraulic fractured wells are completed within the drainage volume of existing producers. Field production and monitoring data show that the existing (parent) wells negatively affect the new (child) wells' productivity, making the new wells produce less than if all the wells are drilled and produced at the same time. This paper presents a systematic study on quantifying the offset well depletion effect in the Permian basin through advanced reservoir modeling and data analytics from field pilots.
The workflow starts with building an Earth model for parent wells in the area of interest, generating a hydraulic fracture network, and performing reservoir simulation for production forecast. The depletion effect, including changes in geomechanical properties on child wells, is properly updated in the Earth model and further captured through fracture geometry distortion and production decrease in the depleted environment. The results from the simulation models are further validated with actual field data. Simulations and pilot studies in both Midland and Delaware basins demonstrate that offset well depletion effect can have significant impact on production performance.
The ability to quantify depletion effect has significant business impacts on development sequence, facility design, completion design, and overall project economics. Analysis on various development scenarios was performed to evaluate the economic impact of depletion effect and determine how to mitigate this effect. Net present value (NPV) economic analysis of different simulation results indicates that time duration of production in the parent well ahead of child well has more impact on depletion effect than the distances between parent and child wells. As examples, depletion quantifications are applied to optimize well production near lease boundary with parent well depletion on the competitor land and guide section development strategy by evaluating pad sequence scenarios.
Experimental Study of Cuttings Transport with Non-Newtonian Fluid in an Inclined Well Using Visualization and ERT Techniques
Authors: Huque Mohammad Mojammel , Imtiaz Syed , Zendehboudi Sohrab , Butt Stephen , Rahman Mohammad Azizur , Maheshwari Priyank 
Published: October 2020

Abstract
Hole cleaning is a concern in directional or horizontal well drilling operation where drill cuttings tend to settle in the lower annulus section. A lab-scale experiments were performed with different non-Newtonian fluids in a 6.16-meter-long, 4.5-inch × 2.5-inch transparent annulus test section to investigate cuttings transport behaviour. This experimental study focused on understanding the cuttings transport mechanism in the annulus section with high-speed imaging technology. The movement of cuttings in the inclined annular section was captured with a high-speed camera at 2000 frames per second. Also, cuttings bed movement patterns at different fluid velocity and inner pipe rotation were captured with a DSLR video camera. The electrical resistance tomography (ERT) system was used to quantify the cuttings volume fraction in the annulus. Different solid bed height and cuttings movement were observed based on fluid rheology, fluid velocity and inner pipe rotation. The mechanistic three layers cuttings transport model was visualized with the experimental procedure. This study showed that solid bed height is significantly reduced with an increase in the inner pipe rotation. This study also identified that cutting bed thickness largely depends on fluid rheology and wellbore inclination. The image from the high-speed camera identified a downward trend of some rolling particles in the annulus due to gravitational force at a low mud velocity. Visual observation from high-speed camera identified a helical motion of solid particles when drill pipe is in contact with solid particles and rotating at higher RPM. Different cuttings movement patterns such as: rolling, sliding, suspension, helical movement, downward movement were identified from the visualization of a high-speed camera.
Oilfield Virtual Twin
Authors: Konchenko Andrey , Chatar Crispin , Doronichev Sergey , Saetern Danny , Bruns Jared 
Published: October 2020

Abstract
Man has always been fascinated by a virtual world outside our reality. From prehistoric drawings to stories told around campfires. In 1838, Charles Wheatstone understood and described binocular vision which demonstrated that the human brain combines two separate images to infer vision. In 1935, seemingly completely separately, Stanley Weinbaum wrote a fictional story in which a person wears a pair of goggles to transport him to a fake world which stimulates the senses. Many of the technology leaders believe that humans will one day create a virtual world indistinguishable from our own reality eventually. Today we already recreate visuals, sounds, touch and even smell. Some companies are working on ways to merge 2-Dimentional(2D) interactions with 3-Dimentional(3D) experiences. Our industry is no different. Many planning and operational experiences have long been done in one way or another but with recent advances in digital technology, there is no reason not to recreate processes and procedures to ensure they are more efficient, bring more value and incorporate richer experiences. As technology advances, reduction in cost are finally making some of what we dreamed about a reality. From the way a geologist reads logs, the way we participate in morning calls while drilling or how we visualize field production and manage facilities in real time, collaborative platforms are evolving with fit for purpose technology.
The concept of a "virtual twin" presumes real-time software—a 2D, 3D, or virtual reality environment where the physical location is being dynamically copied to a necessary degree of details to create a synchronized wellsite representation. Virtual twin is a new concept for the industry. The authors demonstrate the power of visualization, cloud, and virtual reality technology to achieve operational efficiency. Examples demonstrated in the paper are unique and belong to rapidly growing and hugely hyped digital workflows to enable the rethinking of operations and change the status quo for the way of working in the industry. The authors describe three tiers of the virtual twin concept: tier one—planning virtual twin, tier two — dynamic virtual twin, and tier three—prognostic virtual twin. Several examples of concept implementation are demonstrated within oil and gas domains of field development, unconventional completions, drilling, and production gas processing.
Automated Completion System Optimizes Completion Efficiency in Delaware Basin
Authors: Blatt Sam , Douget Phillip , Mast Michael , Wilhoit James , Wiesner Brian , Marvel Tim 
Published: October 2020

Abstract
A new, fully automated completion system contributed to improved safety and efficiency of plug and perforate completions on 34 horizontal wells completed in west Texas Delaware Basin between 2018 and 2020. By enabling nearly continuous frac operations and wireline transitions in three minutes or less, the new system helped reduce the average time-to-complete a well by 38% compared to the average performance on the operator's nearby wells completed using a traditional latch.
Utilizing Artificial Intelligence for Determining Threshold Sand Rates from Acoustic Monitors
Authors: Kolla Srinivas Swaroop , Xu Bohan , Nadeem Ahmed , Luo Qing , Shirazi Siamack A , Sen Sandip 
Published: October 2020

Abstract
Acoustic sand monitors are non-intrusive passive devices and they can be used to provide a warning when sand is being produced in pipelines and/or determine sand rates when calibrated. These devices are attractive to be installed on individual wells or manifolds to detect sand production in offshore production units. One drawback of such devices is the noise created by flow impacting the pipe walls. Thus, if the impact speed or the rate of sand is low, the monitor will not be able to recognize/differentiate the noise that is generated by solid particles impacting pipe walls. The minimum sand rate that can be recognized by the monitor is called a threshold sand rate (TSR). A significant amount of data has been compiled over the years for TSR for various flow conditions, pipe sizes, and sand sizes with stainless steel pipe material. However, it has become challenging to use this data to develop a theoretical model for different flow conditions, pipe, and sand sizes. The goal of this paper is to present an artificial intelligence (AI) approach to determine TSR.
The acoustic sand monitor data utilized in this work has been generated mostly in the lab for sand sizes ranging from 25 to 300 μm varying sand concentrations and pipe diameters 2, 3, and 4 inches in vertical and horizontal directions downstream of stainless-steel elbows. The TSR data, pipe sizes, gas, and liquid rates, and calculated erosion obtained by a mechanistic model (characterizing other parameters that are not directly included) have been used to train and create an AI model that can predict TSR for different flow conditions. The parameters of the AI models, including elastic net, random forest (RF), and support vector machine (SVM), are optimized using nested cross-validation and the model performance is evaluated by the R-squared score.
The AI model was used to predict TSR for several new test conditions collected on a 4-in flow loop. Random samples of past data were also compared to predicted values for further validation.
Additionally, this relationship revealed by the AI model was used to predict field data for TSR data available in the literature. The model agreement with published data is encouraging and suggests that this model can be extended for a variety of flow conditions and pipe sizes not tested before.
This work provides significant TSR data, a framework describing a novel methodology to utilize Artificial Intelligence to correlate the TSR with pipe size, erosion model indicating the severity of impact speed of particles, sand size, and superficial liquid and gas velocities.
Computing Pressure Front Propagation Using the Diffusive Time of Flight in Structured and Unstructured Grid Systems Via the Fast Marching Method
Authors: Hongquan Chen , Tsubasa Onishi , Jaeyoung Park , Akhil Datta-Gupta 
Published: October 2020

Abstract
Diffusive-Time-of-Flight (DTOF), representing the travel time of pressure front propagation, has found many applications in unconventional reservoir performance analysis. The computation of DTOF typically involves upwind finite difference of the Eikonal equation and solution using the fast marching method (FMM). However, application of the finite difference based FMM to irregular grid systems remains a challenge. In this paper, we present a novel and robust method for solving the Eikonal equation using finite volume discretization and the FMM.
The finite volume form of the Eikonal equation is derived by differential manipulation, volume integration and the divergence theorem. Using product rule, the differential term is first converted to the divergence form. Then volume integrals that contain divergence terms are converted to surface integrals using the divergence theorem. Consequently, the spatial coordinates are replaced by cell volumes and transmissibilities which are universal for both structured and unstructured grids in finite volume simulators. When applied with the upstream scheme, the finite volume form evolves into a set of quadratic equations, and fast marching method is implemented to solve these equations.
The implementation is first validated with analytical solutions using isotropic and anisotropic models with homogeneous reservoir properties. Consistent DTOF distributions are obtained between the proposed approach and the analytical solutions. Next, the implementation is applied to unconventional reservoirs with hydraulic and natural fractures. Our approach relies on cell volumes and connections (transmissibilities) rather than the grid geometry, and thus can be easily applied to complex grid systems. For illustrative purposes, we present applications of the proposed method to embedded discrete fracture models (EDFM), dual-porosity dual-permeability models, and unstructured PEBI grids with heterogeneous reservoir properties. Visualization of the DTOF provides flow diagnostics such as evolution of the drainage volume of the wells and well interactions.
The novelty of the proposed approach is its broad applicability to arbitrary grid systems and ease of implementation in commercial reservoir simulators. This makes the approach well-suited for field applications with complex grid geometry and complex well architecture.
3D Visualization of Oil Displacement in Porous Media by the Injection of Microcapsule Suspension using Confocal Microscopy
Authors: Chalhub O. S. Ribeiro Raphael 
Published: October 2020

Abstract
In the past decades, oil companies have shown growing interest in increasing oil recovery efficiency. Commonly, after primary and secondary recovery phases, a large amount of oil remains trapped inside the reservoir. Thus, the number of studies focused on enhanced oil recovery is growing, aiming to increase the recovery factor.
The focus of this work is to study the fundamentals of oil displacement in porous media using a confocal laser scanning microscope, which enables 3D visualization of dynamic phenomena with a good spacial and time resolution. The analysis was on oil displacement that results from the use of a suspension of gellan gum microcapsules in water injected after water injection. These microcapsules moving along with the water, blocked some of the preferential paths and forced the water to displace trapped oil ganglia. The result achieved was a collection of 3D images from artificial porous media, in which it was possible to distinguish the distribution of phases (microcapsules, oil, and aqueous phases) inside the porous media, before and after the microcapsules injection.
These images showed that indeed the gellan gum microcapsules blocked preferential water paths and that, after the blockage, some oil ganglia were displaced from their original positions. This work applied modern techniques of microscopy to investigate the concept behind enhanced oil recovery using microcapsules.
Seismic-Wave Traveltime Computation by Supervised and Unsupervised Training of Artificial Neural Networks
Authors: Grubas Serafim 
Published: October 2020

Abstract
Computation of seismic-wave traveltimes is used in seismic imaging procedures such as Kirchhoff migration. For realistic applications, one has to precompute large traveltime tables (for all sources, receivers, and imaging points). This implies massive computations as well as storage of large files with these traveltime tables. One of the popular traveltime computation methods is a numerical solution of the eikonal equation. In this paper, I addressed the idea of using artificial neural networks for optimizing traveltime computations and using traveltimes in Kirchhoff migration. First, I used supervised learning for approximating and compressing the traveltime tables by artificial neural networks. Second, I used unsupervised learning for solving the eikonal equation.
I used fully-connected neural networks for solving both problems. For the first problem, I used traveltimes precomputed on a coarse for supervised training of a neural network. Synthetic tests show that this neural-network approximation provides great compression of the traveltime tables (102−105 times) with reasonable accuracy of predicting traveltimes on a fine imaging grid. Overall, the use of artificial neural networks results in a speed-up of the Kirchhoff migration operator in two applications: microseismic event localization (by three times) and reflection-seismic migration (by four times).
The second problem was to use artificial neural networks for solving the eikonal equation. The main result was a special design of a loss function that ensures solution of the eikonal equation and allows for neural-network unsupervised training. In the synthetic test, the neural network was successfully used for solving the eikonal equation (forward problem) with slightly better accuracy compared to the first-order Fast Sweeping Method. I also demonstrated that neural networks could also solve the inverse problem – back propagate traveltimes from the observation surface into the subsurface. Such inversion was illustrated by successfully solving the problem of microseismic event localization.
Estimating Petrophysical Properties of Shale Rock Using Conventional Neural Networks CNN
Authors: Misbahuddin Mohammed 
Published: October 2020

Abstract
Digital Rock Physics (DRP) serves as a powerful computational tool for analyzing the petrophysical properties of rock. Obtaining the properties of a fine-grained sample, such as shale is very challenging due to its highly variable and complex nature. Capturing the micro-features of this structure requires advanced microscopy techniques such as SEM (scanning electron microscopy) and FIB-SEM (Focused ion beam- scanning electron microscopy). However, performing advanced microscopy techniques to capture the heterogeneity of the sample is quite difficult; the slow speeds of data collection and analysis are two critical problems that limit more extensive use of this technology. In this study, an alternative approach is proposed to quantify the physical properties of the rock sample. This study aims to accelerate the process of SEM image analysis and reduce the computational cost by using machine learning. A deep learning-based method, Convolutional Neural Networks (CNN), is utilized to predict the properties from the 2D grayscale SEM images of Marcellus shale. The image data set is segmented by applying watershed segmentation to extract the pore network of the sample. Porosity and average pore size are the two properties computed for this study. The SEM images are down sampled to low-resolution images which are fed as an input, and the computed properties are used for training and validation of the CNN network. A detailed description of the image segmentation process, CNN architecture and the predicted results are discussed in this work.
Nano to Macro Scale Investigation into Low Salinity Waterflooding in Carbonate Rocks
Authors: Tetteh Joel T. 
Published: October 2020

Abstract
Low salinity waterflooding (LSWF) in carbonates has proven to improve oil recovery through both fluid-fluid and rock-fluid interactions. However, most of the experimental and modelling studies related to LSWF has been based on macroscale observations such as coreflooding analysis. In this work, a length scale approach from nano- to macro- scale was developed to investigate the underlying mechanisms associated with LSWF and how they impact improved oil recovery (IOR) at each scale.
At the fluid-fluid interface, nanoscale characterization of the oil phase after encountering low salinity brine (∼2,000 ppm) showed the formation of water-in-oil micro-dispersions using Fourier transform infrared (FTIR) spectroscopy and environmental scanning electron microscopy (ESEM). Low interfacial tension (IFT) and high dilatational surface elasticity (DSE) at oil-seawater (∼33,000 ppm) interface resulted in more rigid oil-brine interface as compared to using both formation water (∼160,000 ppm) and low salinity brines. At the microscale, injection of seawater brine through a constricted pore throat suppressed oil snap-off and improved sweep efficiency. Microdispersions were also observed after injecting low salinity brine into oil saturated microfluidic devices during a waterflooding process, resulting in oil remobilization and improving the sweep efficiency.
Reduced electrostatic bond attraction and repulsive disjoining pressure at the crude oil-brine-rock (COBR) interface observed at the nanoscale, contributed to wettability alteration from oil wet to intermediate wet as brine salinity reduced and in the presence of Mg2+ions on the limestone surface. Calcite dissolution accompanied with a spike in brine pH contributed to the reduced electrostatic bond attraction and impacted the wettability state of the rock. These factors at the nanoscale influenced the improved oil recovery at the macroscale through limestone wettability alteration.
This project demonstrated that using a length scale approach provided a detailed understanding of the underlying mechanism influencing the observed wettability alteration and IOR in limestone rocks during LSWF.
Comparison of gluteus medius strength between obese and eutrophic individuals: a cross-sectional study
Authors: Fenato Rafael Ratti , Araujo Allan Cezar Faria , Guimarães Ana Tereza Bittencourt 
Published: October 2020

Abstract
Background: The hip abductor muscles, primarily the gluteus medius, play an important role in stabilizing the pelvis during gait. Gluteus medius weakness is associated with biomechanical changes and musculoskeletal disorders. Obese individuals can have great difficulty maintaining abductor muscular function due to being overweight and possibly experiencing a decrease in muscle mass. However, it is still unclear whether the musculature of obese individuals can compensate for these changes. Therefore, the aim of this study was to compare gluteus medius strength between obese and normal-weight individuals using a digital hand-held dynamometer.Methods: Twenty-five obese (BMI > 35 kg/m2) participants were matched for sex, age, and height with normal-weight individuals. Gluteus medius strength was measured by a single examiner using a belt-stabilized hand-held digital dynamometer placed on the knee of the individuals positioned in lateral decubitus. Three measurements were recorded with rest intervals, and only the highest value measured for each limb was used for analysis. The differences between pairs were calculated, and the normality of the data was assessed using the Shapiro-Wilk test (p < 0.05). The matrices of the variables were standardized and analysed using principal component analysis (PCA).Results: For the strength variables (Newtons) on both sides, no significant differences were detected between the groups (p > 0.05). However, significant differences were detected in these variables between the groups (p < 0.05) when the measurements were normalized to body weight (Newtons/kilograms). PCA indicated that both the absolute and normalized values of strength are lower in obese than in normal-weight individuals.Conclusions: These findings indicate that obese individuals have the same or less strength (PCA) to move more mass, which may suggest a relative weakness that induces functional limitations.Trial registration: The study was approved by the UNIOESTE Human Research Ethics Committee (#1.180.202) in July 2015.