bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hermetically-Sealed Flexible Mobile Robot “MOLOOP” for Narrow Terrain Exploration
Authors: Kimura Hitoshi , Kataoka Mokutaro , Inou Norio ,   Tamagawa University 6-1-1 Tamagawagakuen, Machida city, Tokyo 194-8610, Japan,   Tokyo Institute of Technology 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
Published: April 2022

Conventional mobile robots typically use a wheel or crawler mechanism for locomotion.	However, these robots often get stuck in narrow spaces.	To mitigate this issue, we propose an innovative flexible mobile robot named “MOLOOP” in this study.	The proposed robot has double-looped and hermetically-sealed structure and all ground-contact-area moves to the same direction.	Three synchronized flexible crawlers generate the driving force within the robot body.	The crawler, named “HS crawler,” comprises of loop-connected flexible bags.	The entire body of the robot is flexible; therefore, the robot can pass through narrow terrains with adaptive shape changes.	However, in terms of power and size, performance improvement is necessary for the practical use of the robot.	This paper describes the improvement of HS crawler performance, which directly affects the driving force, traveling speed, and size of the robot.	For the purpose of high input pressure, this study reinforced the flexible bags of the crawler using fibrous materials.	This reinforcement realizes the power-up and downsizing of the flexible bags.	The new crawler achieves a performance of 3.43 Nm torque and 39.0 mm/s traveling speed, compared to a torque of 0.23 Nm and traveling speed of 3.0 mm/s of the previous crawler.	Additionally, the width of the new MOLOOP changed to 270 mm from 420 mm because of the downsized crawler.	Furthermore, a dedicated mechanical valve was developed.	This valve achieves a flow rate of 2.39 L/min as compared to 0.92 L/min of a commercially available solenoid valve.	Moreover, the traveling speed of MOLOOP increased from 3.0 mm/s to 9.0 mm/s.	The prototype robot successfully passed through a narrow terrain with a minimum width of 200 mm (robot width: 270 mm).
Blockchain Database Applications in UIC at Kern County
Authors: Yu John P. , Arredondo Pedro , Cotton Jason 
Published: April 2022

Abstract
Kern County has a population of 900,000 and has been ranked as one of the nation's most productive in both agriculture and petroleum generation. The petroleum industry often requires the injection of water, gas, or other fluids to enhance oil recovery. The possibility of underground water contamination can be of concern in this region where petroleum operations occur alongside drinking water extraction processes. Kern County's petroleum production plays a vital economic role on both a local and national level. Similarly, underground aquifers contain uncontaminated water that is a significant source of municipal drinking water and supply to large-scale agriculture operations. Due to this adjacency, both industry and regulatory agencies closely scrutinize operations to ensure human and environmental health.
The blockchain is a developing peer-to-peer network technology for securely distributing and storing data. A blockchain network consists of several peers, each capable of creating blocks and validating blocks, each with an identical copy of the current blockchain. These blocks consist of transactions, or any type of data record, that have been submitted to the network for validation. Once enough transactions have been submitted, they are bundled into a block which is hashed with the hash of the previous block to create a unique identifier. The regulatory agencies will not be able to make changes except review and approve the project. The block and identifier are then transmitted to every other peer on the network. If the identifier is found to be valid, meaning that the data stored in the sending peer's blockchain has not been altered, the block is added to the receiving peer's local copy. This allows the blockchain to act as immutable ledger, making it an excellent choice for distributing data while maintaining its integrity. Therefore, we can use this secure blockchain network for the underground injection control (UIC) process. We have successfully built a prototype blockchain database and implement an underground injection control (UIC) project in the database. The East Shallow Oil Zone (ESOZ) Fault Block 7A is used for the field case study. All requested documents in the project #22800007 input as the Well Summary GUI and the Regulatory GUI pages for the project review and approval.
State of Art Toolless Production Log for Unconventional Wells Based on Stage Level Pressure Transient Analysis
Authors: Ibrahim Mazher Hassan , Sinkey Matt , Johnston Thomas 
Published: April 2022

Abstract
Hydraulic fracturing has proven to be the only method for making unconventional wells economically productive. Understanding fracture growth is the main industry goal to help in planning well spacing and optimization of fracing operation costs. Traditional production logs used to check the production status at limited snapshot in time by measuring the contribution of each stage. This delays the optimization of fracturing design and production evaluation. Conventional production logging technology uses coiled tubing, fiber or tractor conveyed testing tools to measure key parameters like rate, pressure, temperature, and liquid/gas holdup along the horizontal lateral. From these measurements, the contribution of different lateral parts calculated. It is a mature technology for conventional wells and has been used in unconventional wells in recent years (Lopez, 2014; Nnebocha, 2013; Mccluskey, 2012). However, the cost and interpretation issues in these logs rarely make them economic diagnostics. This paper introduces an innovative production logging technology to gain better understanding of post-frac production performance and help to enhance the development of shale gas wells.
A new method uses real-time integration of rock mechanics during pumping stage and fluid flow in porous media during leakoff to characterize frac stage contribution. This method used to calculate each stage fracture surface area, fracture face skin, stage permeability, and detect stage interference. The stage-level solution used to predict stage-level production performance once well flowback.
Post-stage leakoff analysis used to calculate fracture efficiency of each stage and provide method of improving in real-time. The results from these analyses used in conjunction with full well rate transient analysis (RTA) and build-up analysis (PTA) to create reservoir simulation models with real fracture lengths for each stage. This new method will help in well spacing optimization by informing fracing operations of fracture interference with offset wells, both parent and child in real-time. The result can be validated with production logs, fiber optics, and tracer analysis.
Use of real-time toolless production log does not require running of any surveillance tool in well beside surface pressure gauge, therefore the cost is minimal compared to current industry methods. Also, the new method will lead to saving millions of dollars to operator if it uses in real time frac job to guide the next frac optimization.
Ineffectiveness of Acid Fracturing for Stimulating a Tight Fractured Reservoir Rock, A Case Study
Authors: Al-Enezi Huda Rajab , Ershaghi Iraj 
Published: April 2022

Abstract
Stimulating source rocks using hydraulic fracturing in unconventional source rocks is a common process. But such practice has not been followed when dealing with tight reservoir rocks. Tight carbonate reservoir rocks have in general been subjected to multistage acid fracturing. This paper describes the analysis of actual performance data to acid fracturing in a tight carbonate formation and the ineffectiveness of the process as measured by the performance responses of the producing wells. The case study relates to a tight formation that is considered a reservoir rather than a source rock. The formation permeability is in the range of 0.1-5 millidarcies. Development has been through numerous horizontal wells with limited multistage acid stimulation. Analysis of performance data for more than 30 wells Indicates no fracture flow and very limited stimulated production.
Different diagnostic methods were used to determine the nature of the flow regime and to examine the true benefit of multistage acid fracturing. Most of the wells studied show primarily a radial flow condition after limited acid fracturing. The main observation is that the process of acid fracturing at best has resulted in local near wellbore stimulation on all these wells, and there are no significant indications of fracture-dominated flow. Many of the wells also show early signs of the boundary effect that explains the ineffectiveness of the recovery process.
Effective Neural Networks Models for Inferred Production Prediction in ESP Equipped Wells
Authors: Popa Andrei , Umbriaco Jacob , Tirtawidjaja Alexia 
Published: April 2022

Abstract
Well production measurement is critical for successful reservoir management and effective decision making to optimize day-to-day field production. The production measurement or well gauging is achieved through physical measurement using Automatic Well Testing (AWT) sites or manually using portable measuring units. When the AWT's are not operational, or in the case when measurements are lacking due to different reasons, well production data points become sparce and can lead to missed production opportunities, poor production allocation and even impact the reserves reporting.
The article introduces an Artificial Intelligence approach that uses a neural network to successfully predict the daily well production rates for Electrical Submersible Pumps (ESP) operated wells in the absence of physical measurements. The methodology was applied in a brown field operated under waterflooding recovery mechanism. The predicted well production rates and intelligent guardrail controls were implemented in an established production monitor tool. The tool allows operation by exception through the use of a dashboard to alarm when well problems occur.
This work demonstrated how digital technologies can either augment, or to some extent, replace the physical measurements from external devices or processes contributing to improved monitoring, better decision making, and cost reduction. The general example presented shows the value of the technology by generating critical information that helps quickly identify abnormal behaviors such as lost production and prompts timely corrective actions which ultimately lead to field optimization.
Optimizing Unconventional Hydraulic Fracturing Design Using Machine Learning and Artificial Intelligent
Authors: Alhemdi Aymen , Gu Ming 
Published: April 2022

Abstract
For optimizing the hydraulic fracture design in shales, it is challenging to understand the impact of several different parameters on fracture propagation and production, such as geomechanical properties and fracturing treatment parameters. Current frac simulators do not exhibit consideration of the anisotropy of rock elasticity in the shales. Additionally, using the fracture simulation linked with reservoir simulation for the parametric study is low efficient.
Due to its lamination nature, shale has different geomechanical properties along with the directions vertically and horizontally. Anisotropic elastic properties and stresses lead to more complications for predicting the fracture. This study introduces a comprehensive workflow for fracturing design optimization by applying supervised machine learning. The research also aims to develop an algorithm that can help any shale reservoir optimize the pumping treatment design of hydraulic fracture.
The workflow is divided into six steps. Firstly, acoustic and density logs for a research well in Marcellus shale are used to interpret Young's modulus, Poisson's ratio, and minimum horizontal stress magnitude by anisotropic VTI model. In step 2, the interpreted mechanical properties, including the current treatment design of the target well, are inserted into the frac simulator to obtain the conductivity distribution inside the fracture. The conductivity distribution converts to fracture permeability matrix. As for the third step, the fracture permeability matrix is consequently entered into the reservoir model for estimating the production. The output production is matched with the field history data. For the fourth step, a random sampling algorithm is applied to build a database with a rational sample size. In step 5, the generated database is employed to train and validate an artificial neural network model (ANN). Lastly, parametric studies are performed through the trained ANN model to analyze the multi-parameter effect on cumulative production.
This workflow can predict the early and late production for a given fracture design based on multiple fracture treatment parameters such as initial fracture depth, cluster numbers of each stage, and proppant type. Besides, the study provides a capability for multivariable analysis to better understand the productivity behavior of the fractured well.
Production Forecasting in Conventional Oil Reservoirs Using Deep Learning
Authors: Temizel Cenk Temizel , Odi Uchenna , Al-Sulaiman Nouf , Reddy Karri , Putra Dike , Yurukcu Mesut , Aydin Hakki , Yegin Cengiz 
Published: April 2022

Abstract
Accurate estimation of the estimated ultimate recovery (EUR) is critical in decision making processes related to the development of conventional oil reservoirs. Existing methods have limitations when it comes to predicting such long-term production behaviors. This study analyzes the performance of deep learning methods such as long short-term memory (LSTM) neural networks on time-series data, and their effective application to accurately estimate the EUR in conventional reservoirs.
Synthetic data that are realistic and representative of many major conventional oil reservoirs were generated for this study. The generated dataset was used by the LSTM model for the purpose of forecasting the EUR. The results of the LSTM model were compared with that of a reservoir simulation model from a full-physics reservoir simulator. EUR forecasts from the physics-based reservoir simulator is used as a benchmark and the LSTM model shows a good predictive accuracy while forecasting the long-term production behavior from a well in a conventional oil reservoir. The LSTM model-based deep learning method can be effectively used with real-field data obtained from wells in conventional reservoirs to accurately predict the EUR, and the study provides a comparative analysis of the results and factors affecting the EUR forecasts from the LSTM model and reservoir simulation model.
Deep learning methods such as LSTMs have an inherent advantage in identifying trends in time-series data and making forecasts using the data. The existing literature has a limited number of studies that outline the use of deep learning methods for EUR forecasts and this study covers this gap by providing details analyses, best practices and workflows on the use of such methods for conventional oil reservoirs.
Reservoir Simulation Framework to Evaluate the Potential Benefit of Radial Jet Drilling Technology Accounting for the Risk of Irreversible Radial Collapse
Authors: Torrealba Victor A. , Rey Alvaro , Chambers Geordie , Goodman Michael , Kubitza Andrew 
Published: April 2022

Abstract
This study presents a reservoir simulation framework to investigate the oil production uplift performance of Radial Jet Drilling (RJD) technology for a naturally fractured diatomite reservoir undergoing waterflooding. The findings from the study can help better design a field trial and plan Surveillance, Analysis and Optimization (SA&O) activities. The framework allows for the flexible definition of various parameters that control the topology of the RJD well (including number penetrated layers, number of radials per layer, and radial segment length) as well as the pressure drop along the radial segment (including the segment roughness and hydrodynamic diameter). The framework relies on advanced wellbore modeling capabilities that compute the pressure drops inside the well; this allows for the consideration of radial segment collapse whenever the radial segment pressure is below a radial collapse pressure.
The simulated behavior relied on a dual porosity dual permeability (DPDK) reservoir model that had been history-matched for primary depletion and waterflooding over a cumulative 72-year period. The RJD well oil production performance is evaluated over a 16-year period controlled with a bottomhole pressure constraint. The model is calibrated to representative type curves in the absence of radials (perforations only case) and in the presence of radials for a specified topology. Once the model has been calibrated, 162 simulation cases are considered to evaluate the sensitivity of the oil production uplift to various model parameters and operational conditions.
Radial segment length, radial collapse pressure and number of penetrated layers showed the greatest impact on oil production uplift. Increasing radial segment length and number of penetrated layers and decreasing the radial collapse pressure led to an increase in oil production uplift. We introduced a cumulative radial segment length metric that accounts for the impact of number of penetrated layers, number of radials per layer, and radial segment length. For a fixed cumulative radial segment length, configurations with a higher number of penetrated layers and a lower number of radials per layer led to a higher oil production uplift.
The simulation tool and framework developed can be used to assess the potential benefit of the RJD technology, including risks arising from radial segment collapse. For all radial collapse pressure scenarios, a gentle drawdown strategy proved to be the most consistent in terms of oil production uplift performance. Production performance monitoring (e.g., via dedicated test separators) can help identify major radial collapse events as evidenced by discontinuous trends in the oil production rate, gas/oil ratio, and/or water cut.
Effective Well Rate Management Using a Streamline-Based Model in Belridge Diatomite Waterflood
Authors: Guerrero-Lee Marilou Tanchuling , Manfre Diego , Thiele Marco Roberto 
Published: April 2022

Abstract

Summary
In the early 1980's, waterflooding began in Belridge Diatomite (BD), a thick, highly porous, and tight reservoir in California. Over the years, differing injection strategies lead to mixed results. Today, there are approximately 1,900+ injectors and 2,600+ producers in the Belridge Diatomite Waterflood (BDWF) and setting injection and production well rate targets to improve recovery is extremely challenging. Multiple teams using different methods for estimating rate targets in different parts of the field adds complexity to the overall field management strategy. The main objective of this work was to simplify field management by using a quantitative, streamline-based method to establish and quantify injector-producer relationships, reduce human bias, while improving the efficiency and flood performance in the West Grande (WG) area of the BDWF subject to key surface and subsurface constraints.
The pilot test targeted 52 injection strings and 142 producers in Southern WG with an approximate total water injection rate of 10,500 STB/Day and total water and oil production rates of 8,200 STB/D and 700 STB/D, respectively. An important and novel aspect of the work presented here involves constraining the injection well rate targets to surface elevation, injection string communication, 30-day average wellhead pressure, subsurface impairments, and surface injection capacity. On the production side, the constraints were surface elevation, pump runtime, and operational status. This work describes setting well rate targets using a streamline-based workflow while honoring minimum and maximum rates as deduced from the constraints above.
Injection and production rates in the WG pilot area were changed four times over nine months in the period May 2020-January 2021. Not withstanding uncertainties in production and watercut measurements and interference due to operational activities, the injection and production rate changes resulted in a reversal of the oil decline observed in the previous year. The improvement in the oil decline honored all key constraints and did not cause WG to experience changes to surface elevation, an important requirement in reservoir management of the highly compressible Diatomite reservoir.
Using streamlines to define well patterns based on historical production/injection rates, and considering all patterns simultaneously is a major departure from the one-at-a-time fixed pattern and reservoir team-specific strategy used in the past. Considering that the manual pattern-by-pattern review consumed well over 50% of the time spent by teams trying to improve flood performance, the approach described in the work also represents a significant improvement in productivity and a more agile reservoir management strategy. The ability to include key surface and subsurface constraints in the calculation of well target rates is a novel addition to streamline-based surveillance modeling and a key contribution of this work.
Integrated Workflow to Investigate the Effect of Fracture Interference on Well Performance
Authors: Ibrahim Ahmed Farid 
Published: April 2022

Abstract
Horizontal drilling and multistage hydraulic fracturing applied in shale formations over the past decade. Economic productivity was achieved by generating a large fracture surface area. The cluster spacing was considered up to 700 ft in Barnett and Bakken shale formations. At this time, tight cluster spacing has been used up to 15 ft apart in Eagle Ford and DJ Basin, moreover, the operators are trying even closer cluster spacing. The drive for applying tight fracture spacing is to increase the initial production rate. However, a higher initial production rate is at the expense of higher operation and completion cost in addition to operation complexity.
This study presents an integrated workflow to investigate the effect of cluster interference on well performance. Analytical rate transient analysis (RTA) was combined with reservoir numerical simulation to calculate the effective fracture surface area for hydrocarbon production. The ratio of the effective fracture surface area from RTA analysis to the actual stimulated fracture area from the numerical simulation will be correlated to the cluster spacing. A proxy model was built to estimate the effective to actual stimulated fracture area as a function of completion and reservoir parameters. Finally, the integrated workflow was applied in actual field data for two gas-shale wells.
The results showed that the well with a higher stage number and tighter cluster spacing will have high cluster interference with low effective to actual fracture surface area ratio. In addition, the well will drain the production area near the wellbore faster with the high initial production rate but with high production declining rate. Increasing the cluster spacing, with the same injected proppant volume, shows an increase in the effective to actual fracture surface area ratio, and low cluster interference. A lower initial rate was observed with a low production declining rate.
The results from this study will assist the completion and reservoir engineers to optimize the cluster spacing to maximize the well revenue.