bluuebunny/crossref_metadata_2025_split huggingface dataset:
I-Geo Sensing Fracture Diagnostic i-GSFD for Fast Processing of the Smart Microchip Proppants Data
Authors: Pham Vuong Van , Dahaghi Amirmasoud Kalantari , Negahban Shahin , Fincham William , Babakhani Aydin 
Published: September 2021

Abstract
Unconventional oil and gas reservoir development requires an understanding of the geometry and complexity of the hydraulic fractures. The current categories of fracture diagnostic approaches include methods for near-wellbore (production & temperature logs, tracers, and borehole imaging) and far-field techniques (micro-seismic fracture mapping). These techniques provide an indirect and interpreted fracture geometry. Therefore, none of these methods consistently provides a fully detailed and accurate description of the characteristic of the subsurface hydraulic fractures. This study proposes a novel approach that uses direct data from the injected fine size (proppant Mesh size equivalence) and battery-less, Smart MicroChip Proppants (SMPs), to map the fracture geometry. This novel approach enables direct, fast, smart, and real-time processing of the received high-resolution geo-sensor data from the SMPs collected in high pressure and high-temperature environment and maps the fracture network using the proposed i-Geosensing Fracture Diagnostic (i-GSFD), which is a closed-loop architecture. i-GSFD is based on multi-dimensional projection unsupervised clustering, and an integrated Bayesian optimizer to control the stochastic nature of any components in the loop.
An Advanced Proppant Depositional Study with Post-Production Flow Evaluation in a 10' X 20', Transverse Fracture, Slot Flow Configuration
Authors: Donald A. Anschutz , Patrick J. Wildt , K. Michelle Stribling , Jim Craig , Luiz R. Curimbaba , Pedro Silva , Ibrahim S. Abou-sayed 
Published: September 2021

Abstract
While the shale revolution flourished prior to the pandemic, the increased supply bubble had already taken a toll on the profitability of horizontal wells with multiple transverse fractures. A significant shift previously occurred to reduce proppant costs by utilizing cheaper, smaller grained, lower strength, and broadly diverse grain sized sands. Due to the extremely low matrix permeability in active unconventional plays, the use of regional 40/70 and 100 mesh sands (50/140, 70/140, etc.) has become commonplace with adequate results. What remains is the need for enhanced conductivity near the wellbore to handle the radial flow convergence loss when the well is brought on-line. Research is being conducted to better understand how to efficiently increase near-wellbore conductivity using lead and tail-in stages with higher permeability (ceramic) proppant when frac sand is the majority of the material pumped into the well. A 10’x20’ Large Slot Flow (LSF) apparatus, equipped with multiple injection points, side-panel ports for leak-off and/or post-test injection, with the ability to be disassembled for sample analysis after testing, was utilized for this project. For this data, the inlet was moved to the centerline of the wall to allow for proppant and fluid to transport into an environment similar to a horizontal wellbore connecting with a transverse fracture. Various tests were conducted to study the depositional characteristics of lead and tail-in stages with ceramic proppant (15% BW-Lead, 5% BW-Tail) and a main stage of 100 mesh sand (80%). Three inlet positions were established in the lower, middle, and upper portion of the apparatus. Tests were recorded to visually capture the efficiency of placing the premium proppants near the wellbore for increased conductivity. A key addition to the study was the innovative, post-production analysis through the side-panel ports. Fluid was injected into the proppant pack to observe the effect of increased near-wellbore conductivity. To improve visibility, the fluid was colored with a fluorescent dye and observed under black lights. The injection front geometry was radial initially, but typically elongated toward the exit point after contacting the ceramic proppant. The amount of time and distance for the fluid to travel through the sand pack, as well as that for the fluid to reach the offtake point once the ceramic bed was reached, were monitored and recorded. The ratio of the velocities should represent a valid qualitative indication of the conductivity contrast of the two proppants.
This paper will describe the unique experimental configuration, outline the testing program for both deposition and post-production assessments performed on the deposits, along with results that could provide better design practices leading to improved transverse fracture performance.
CO2 Leakage Rate Forecasting Using Optimized Deep Learning
Authors: He Xupeng , Zhu Weiwei , Santoso Ryan , Alsinan Marwa , Kwak Hyung , Hoteit Hussein 
Published: September 2021

Abstract
Geologic CO2 Sequestration (GCS) is a promising engineering technology to reduce global greenhouse emissions. Real-time forecasting of CO2 leakage rates is an essential aspect of large-scale GCS deployment. This work introduces a data-driven, physics-featuring surrogate model based on deep-learning technique for CO2 leakage rate forecasting. The workflow for the development of data-driven, physics-featuring surrogate model includes three steps: 1) Datasets Generation: We first identify uncertainty parameters that affect the objective of interests (i.e., CO2 leakage rates). For the identified uncertainty parameters, various realizations are then generated based on Latin Hypercube Sampling (LHS). High-fidelity simulations based on a two-phase black-oil solver within MRST are performed to generate the objective functions. Datasets including inputs (i.e., the uncertainty parameters) and outputs (CO2 leakage rates) are collected. 2) Surrogate Development: In this step, a time-series surrogate model using long short-term memory (LSTM) is constructed to map the nonlinear relationship between these uncertainty parameters as inputs and CO2 leakage rates as outputs. We perform Bayesian optimization to automate the tuning of hyperparameters and network architecture instead of the traditional trial-error tuning process. 3) Uncertainty Analysis: This step aims to perform Monte Carlo (MC) simulations using the successfully trained surrogate model to explore uncertainty propagation. The sampled realizations are collected in the form of distributions from which the probabilistic forecast of percentiles, P10, P50, and P50, are evaluated. We propose a data-driven, physics-featuring surrogate model based on LSTM for CO2 leakage rate forecasting. We demonstrate its performance in terms of accuracy and efficiency by comparing it with ground-truth solutions. The proposed deep-learning workflow shows promising potential and could be readily implemented in commercial-scale GCS for real-time monitoring applications.
Jointed Tubing Injector Snubbing on Extended Reach Wells
Authors: Miller H. J. , Richard A. D. 
Published: September 2021

Abstract

OBJECTIVE / SCOPE
An injector has been developed that is able to continuously move conventional jointed tubing in and out of wells that may be underbalanced. It is an advantage to use the jointed tubing injector rather than coiled tubing or conventional hydraulic snubbing due to cost, speed of operation, transportation, effectiveness, and safety. The paper will describe the function and application of the jointed pipe injector.

METHODS, PROCEDURES, PROCESS
An injector has been designed with retractable gripping segments integral to the gripper blocks that are able to function on conventional jointed tubing, over interconnecting couplings and with the advantages of continuously operating injector movement. The description is to include how the geometry of the retractable gripper block system works and how the technical and safety risks of a conventional snubbing system or coiled tubing are overcome. Configurations whereby the jointed tubing injector can be used to provide methods of completing wells that are safer and more efficient than coiled tubing or a conventional hydraulic snubbing jack will be presented.

RESULTS, OBSERVATIONS, CONCLUSIONS
The biggest limitation of coiled tubing is due to its size and residual bend, it is not capable of reaching the end of the well before the wellbore friction causes helical buckling. The OD of the coiled tubing is limited by the available reel sizes and the difficulty transporting the large reels due to road dimensional and weight limitations. Coiled tubing is not able to be rotated at any time in the well. The use of jointed tubing eliminates those limitations.
When a well is being completed with a conventional hydraulic snubbing jack, the length of the stroke that the jack can take is limited by the allowable unsupported length of the tubing to ensure that it will not buckle. It is also forced to stop workstring movement each time the jack is reset therefore the static friction of the workstring must be overcome during each movement of the jacks. The design of the jointed tubing injector minimizes the unsupported length of the tubing and allows the continuous movement of the tubing. The operation is less labor intensive, and the controls can be moved to a position that is less exposed to danger.

NOVEL / ADDITIVE INFORMATION
The Jointed Tubing Injector can continuously move jointed tubulars in and out of a well. There is no other piece of equipment that will address as many of the problems that have been experienced in the completion of extended reach wells. The paper will describe the injector and control system and how it can be applied to solve the challenges.
Robust CO2 Plume Imaging Using Joint Tomographic Inversion of Distributed Pressure and Temperature Measurements
Authors: Yao Changqing , Chen Hongquan , Onishi Tsubasa , Datta-Gupta Akhil , Mawalkar Sanjay , Mishra Srikanta , Pasumarti Ashwin 
Published: September 2021

Abstract
Geologic CO2 sequestration and CO2 enhanced oil recovery (EOR) have received significant attention from the scientific community as a response to climate change from greenhouse gases. Safe and efficient management of a CO2 injection site requires spatio-temporal tracking of the CO2 plume in the reservoir during geologic sequestration. The goal of this paper is to develop robust modeling and monitoring technologies for imaging and visualization of the CO2 plume using routine pressure/temperature measurements.
The streamline-based technology has proven to be effective and efficient for reconciling geologic models to various types of reservoir dynamic response. In this paper, we first extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process.
The power and utility of our approach are demonstrated using both synthetic and field applications. We first validate the streamline-based DTS data inversion using a synthetic example. Next, the hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warmback analysis of the temperature data.
The novelty of this work is the streamline-based history matching method for the DTS data and its field application to the Department of Engergy regional carbon sequestration project in Michigan.
Dull Bit Grading Using Video Intelligence
Authors: Ekeregbe Merit P. , Khalaf Mina S. , Samuel Robello 
Published: September 2021

Abstract
Although visual data analytics using image processing is one of the most growing research areas today and is largely applied in many fields, it is not fully utilized in the petroleum industry. This study is inspired by medical image segmentation in detecting tumor cells. This paper uses a supervised Machine Learning technique through video analytics to identify bit dullness that can be used in the drilling industry in place of the subjective screening approach. The evaluation of bit performance can be affected by subjective evaluation of the degree of dullness. The present approach of using video analytics is able to grade bit dullness to avoid user subjectivity.
The approach involves the use of datasets in good quantity and quality by separating them into training datasets, testing datasets, and validation datasets. Due to the large datasets, Google Collaboratory was used as it provides access to its Graphic Processing Unit (GPU) online for the processing of the bit datasets. The processing time and resource consumption are minimized using Google GPU. Using the Google GPU resources, the procedure is automated without any installation. After the bit is pulled out and cleaned, a video is taken around and up and down in 360°. Further, it is compared against the green bit. By this approach, multiple video datasets are not required.
The algorithm was validated with new sets of bit videos and the results were satisfactory. The identification of the dullness or otherwise of each screened bit is done with the aid of a bounding box with a stamp of a level of confidence (range 0.5–1) and the algorithm assigns for its decision on the identified or screened object. This method is also able to screen multiple bits stored in a single place. In an event where several drill bits are to be screened, manual grading will be a huge task and will require a lot of resources. This model and algorithm will take a few minutes to screen and provide grading for several bits while videos are passed through the algorithm. It has also been found that the grading with the video was much better than the single image as the contextual information extracted are much higher at the level of the entire video, per segment, per shot, and per frame. Also, methodology is made robust so that the video model test starts successfully without error. The time penalty for the processing is fast and it took less time for a single video screening.
The work developed here is probably the first to handle the dull bit grading using video analytics. With more of these datasets available, the future automation of the IADC bit characterization will soon evolve into an automated process.
Identification of Shale Bedding Layers from Micromechanical Evaluation
Authors: Cheng Shizhong , Sheng Mao , Chen Zhaowei , Tian Shouceng , Li Gensheng 
Published: September 2021

Abstract
Bedding layers are well-developed in shale and recognized to enhance the complexity of the hydraulic fracture network. A micromechanical evaluation approach of shale bedding layers based on indentation testing techniques was proposed. Rock thin section identification and scanning electron microscope were utilized to describe the shale laminations. To capture high-resolution image with large area and ensure abundant morphology and mineral information, Atlas 5 system, which could create large mosaic images, was applied. Combined with FIB, a method for positioning the laminations in SEM and accurately navigate to it in the micromechanics experiment was established. Fractures were emphasized to be initiated and extend in the clay mineral layers. It can be explained that the flaky structure of clay mineral is beneficial for the extension of fracture. It will become more potential to connect with the fluid-driven fractures. Nanoindentation was conducted to obtain micromechanical properties in different lamination layers. Results verify the feasibility of this scheme and demonstrate fine-grained, transition and coarse-grained layers. Indentation results show variation in micromechanical properties of coarse-grained layer, the transition layer and the fine-grained layers. The present work is helpful to identify shale bedding layers from micromechanical evalutions.
Development of a Virtual Flowmeter as an Enhanced Alternative for Field Production Monitoring
Authors: Chaves Gabriela , Karami Hamidreza , Monteiro Danielle , Martins Ferreira Virgilio José 
Published: September 2021

Abstract
Flowrate is a valuable information for the oil and gas industry. High accuracy on flowrate estimation enhances production operations to control and manage the production. Recognized as a cost-efficient solution, the VFM (virtual flowmeter) is a mathematical-based technology designed to estimate the flowrates using available field instrumentation. The VFM approach developed in this work combines black-box simulations with mixed-integer linear programming (MILP) problem to obtain the flowrates dismissing the tuning process. The methodology included a set of multiphase flow correlations, and the MILP was developed to estimate the flowrate and designate the best fit model. We evaluated the proposed VFM against 649 well test data. The methodology presented 4.1% average percentage error (APE) for percentile 25% and 13.5% APE for percentile 50%. We developed a VFM technology to be used in scenarios with a lack of data, and we believe that our tuning-free method can contribute to the future of VFM technologies.
Benchmarking and Field-Testing of the Distributed Quasi-Newton Derivative-Free Optimization Method for Field Development Optimization
Authors: Alpak Faruk , Wang Yixuan , Gao Guohua , Jain Vivek 
Published: September 2021

Abstract
Recently, a novel distributed quasi-Newton (DQN) derivative-free optimization (DFO) method was developed for generic reservoir performance optimization problems including well-location optimization (WLO) and well-control optimization (WCO). DQN is designed to effectively locate multiple local optima of highly nonlinear optimization problems. However, its performance has neither been validated by realistic applications nor compared to other DFO methods.
We have integrated DQN into a versatile field-development optimization platform designed specifically for iterative workflows enabled through distributed-parallel flow simulations. DQN is benchmarked against alternative DFO techniques, namely, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method hybridized with Direct Pattern Search (BFGS-DPS), Mesh Adaptive Direct Search (MADS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). DQN is a multi-thread optimization method that distributes an ensemble of optimization tasks among multiple high-performance-computing nodes. Thus, it can locate multiple optima of the objective function in parallel within a single run. Simulation results computed from one DQN optimization thread are shared with others by updating a unified set of training data points composed of responses (implicit variables) of all successful simulation jobs. The sensitivity matrix at the current best solution of each optimization thread is approximated by a linear-interpolation technique using all or a subset of training-data points. The gradient of the objective function is analytically computed using the estimated sensitivities of implicit variables with respect to explicit variables. The Hessian matrix is then updated using the quasi-Newton method. A new search point for each thread is solved from a trust-region subproblem for the next iteration. In contrast, other DFO methods rely on a single-thread optimization paradigm that can only locate a single optimum. To locate multiple optima, one must repeat the same optimization process multiple times starting from different initial guesses for such methods. Moreover, simulation results generated from a single-thread optimization task cannot be shared with other tasks.
Benchmarking results are presented for synthetic yet challenging WLO and WCO problems. Finally, DQN method is field-tested on two realistic applications. DQN identifies the global optimum with the least number of simulations and the shortest run time on a synthetic problem with known solution. On other benchmarking problems without a known solution, DQN identified compatible local optima with reasonably smaller numbers of simulations compared to alternative techniques. Field-testing results reinforce the auspicious computational attributes of DQN. Overall, the results indicate that DQN is a novel and effective parallel algorithm for field-scale development optimization problems.
A Dual-Directional Flow Control Device for Cyclic Steam Stimulation CSS Applications
Authors: Zhu Da 
Published: September 2021

Abstract
Cyclic steam stimulation (CSS) is one the most effective thermal recovery methods. It is widely used as the primary thermal recovery method to recovery heavy oil fields in Middle East, Asia Pacific, North and South America. In this paper, a novel dual-directional flow control device (FCD) will be introduced. This FCD technology can allocate accurate steam outflow into the reservoir formation and improve steam quality during steam injection period and can mitigate steam breakthrough from the neighboring wells during production period.
In the first section, we will give a brief introduction on CSS and the main issues encountered in the field operation. A multi-directional flow control nozzle specifically designed for CSS application will be presented. Design philosophy in thermodynamics and hydrodynamics of the nozzle will be discussed in detail. Field performance results, Computational Fluid Dynamics (CFD) and flow loop testing data will be shown to evaluate the performance of the technology. The application of the technology in steam assisted thermal applications will be introduced. Well-known issues such as erosion and scaling on the FCD tools will be studied in the end.