bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and Experimental Research of a Fiber-Optic Communication Module for Well Logging
Authors: Wang Yue , Li Bo , Sun Lei , Hao Fenghuan , Rourke Marvin 
Published: September 2021

Abstract
Fiber-optic transmission has been applied in oil and gas industry over the years. Compared with other methods applied in the industry, fiber-optic transmission has the advantages of low loss, long-distance, high-capacity and robust to the electromagnetic interference. The ability to provide reliable transmission systems in the harsh environments like high temperatures is the key driver for the continued use of fiber-optic communication for in-well applications.
We design a fiber-optic communication system under high temperatures for well logging applications. It consists of high-temperature laser diode, high-temperature photodetector with photoelectric detection circuit, drive control circuit, and field-programmable gate array (FPGA) as the communication chip. This system ensures that data can be transmitted at a rate of 15 Mbps at temperatures up to 155°C. The FPGA board makes the system to control data transmission flexibly and enable the serial communication between the photoelectric module and the host computer. Additionally, the number of fibers used in fiber-optic communication in logging will be reduced to only a single fiber for transmitting and receiving.
A series of experiments on the performance and effects of fiber-optic communication at different temperatures was carried out. Data transceiver tests and eye diagram tests are presented. The experimental results demonstrated that this fiber-optic communication system is capable of working steadily over a long period of time in harsh environments around 155°C to realize broadband and remote transmission of logging information.
This system provides a way that allows optical information to transmit in a high-temperature environment. It can be applied to well logging and fiber-optic sensing (e.g., real-time environmental parameters transmission, fiber-optic well monitoring) for developing real-time, high-data-rate, bidirectional fiber-optic communication in the future.
Experiments of Liquid Loading and Intermittent Production Using a Lab-Scale Reservoir-Tubing Setup
Authors: van 't Westende Jos , van Nimwegen Dries , Belfroid Stefan , Slot Harmen 
Published: September 2021

Abstract
Experiments were performed to investigate the physics behind intermittent production and liquid loading, using a setup containing a reservoir model coupled to a vertical production tubing. In the experiments both gas and liquid are injected into the reservoir, which is a container in which sand with two different permeabilities is placed. Quick closing valves are incorporated into the experimental setup in order to simulate well shut-ins and start-ups. The experimental results show that the addition of the reservoir to the experimental set-up shifts the minimum in the total pressure losses over the system to lower gas flow rates as the permeability of the reservoir decreases. When performing shut-ins where a significant liquid column is present in the tubing, as is the case in liquid loaded wells, performing a sufficiently long shut-in can lead to the deliquification of the system.
Automating the Log Interpretation Workflow Using Machine Learning
Authors: Sankaranarayanan Bhuvaneswari , Abubakar Aria , Allen David F. , Diaz Granados Ivan 
Published: September 2021

Abstract
Log interpretation is the task of analyzing and processing well logs to generate the subsurface properties around wells. A direct application of machine learning (ML) to this task is to train an ML model for predicting properties in target wells given well logs (data) and properties (labels) in a set of training wells in the same field and/or region.
Our ML model of choice for predicting the desired properties is the decision tree-based learning algorithm called random forests (RF). We also devise a mechanism to automatically tune the hyperparameters of this algorithm depending on the data in the training wells. This eliminates the tedious task of carefully tuning the hyperparameters for every new set of training wells and provides a one-click solution. In addition to predicting the properties, we compute the uncertainty in the predicted properties in the form of prediction intervals using the concept of quantile regression forests (QRF).
We test our workflow on two use cases. First, we consider a petrophysics use case on an unconventional land dataset to predict the petrophysical properties such as water saturation, total porosity, volume of clay, and total organic carbon from petrophysics logs. Then, we consider a geomechanics use case on a conventional offshore dataset to predict the lithology, pore pressure, and rock mechanical properties. We obtain a good prediction performance on both use cases. The uncertainty estimates also complement the ML model's prediction of the properties by explaining the various correlations that are found to be existing among them based on domain knowledge.
The entire workflow of automating the tuning of hyperparameters and training the ML model to predict the properties along with its estimate of uncertainty provide a complete solution to apply the ML workflow for automated log interpretation.
A Generalized Model for the Field Assessment of Drilling Fluid Viscoelasticity
Authors: Hongbo Chen , Temi Okesanya , Ergun Kuru , Garett Heath , Dylan Hadley 
Published: September 2021

Abstract
Recent studies highlight the significant role of drilling fluid elasticity in particle suspension and hole cleaning during drilling operations. Traditional methods to quantify fluid elasticity require the use of advanced rheometers not suitable for field application. The main objectives of the study were to develop a generalized model for determining viscoelasticity of a drilling fluid using standard field-testing equipment, investigate the factors influencing drilling fluid viscoelasticity in the field, and provide an understanding of the viscoelasticity concept.
Over 80 fluid formulations used in this study included field samples of oil-based drilling fluids as well as laboratory samples formulated with bentonite and other polymers such as partially-hydrolyzed polyacrylamide, synthesized xanthan gum, and polyacrylic acid. Detailed rheological characterizations of these fluids used a funnel viscometer and a rotational viscometer. Elastic properties of the drilling fluids (quantified in terms of the energy required to cause an irreversible deformation in the fluid's structure) were obtained from oscillatory tests conducted using a cone-and-plate type rheometer. Using an empirical approach, a non-iterative model for quantifying elasticity correlated test results from a funnel viscometer and a rotational viscometer.
The generalized model was able to predict the elasticity of drilling fluids with a mean absolute error of 5.75%. In addition, the model offers practical versatility by requiring only standard drilling fluid testing equipment to predict viscoelasticity. Experimental results showed that non-aqueous fluid (NAF) viscoelasticity is inversely proportional to the oil-water ratio and the presence of clay greatly debilitates the elasticity of the samples while enhancing their viscosity.
The work efforts present a model for estimating drilling fluid elasticity using standard drilling fluid field-testing equipment. Furthermore, a revised approach helps to describe the viscoelastic property of a fluid that involves quantifying the amount of energy required to irreversibly deform a unit volume of viscoelastic fluid. The methodology, combined with the explanation of the viscoelasticity concept, provides a practical tool for optimizing drilling operations based on the viscoelasticity of drilling fluids.
Single Trip Deployment of Multi-Stage Completion Liners Through the Used of Interventionless Flotation Collars
Authors: Tait William , Munawar Mohammed 
Published: September 2021

Abstract
Due to challenging market conditions, the drilling and completion industry has needed to put forth innovative deployment strategies in horizontal multi-stage completions. In difficult wellbores, the traditional method for deploying liners was to run drill pipe. The case studies discussed in this paper detail an alternative method to deploy liners in a single trip on the tieback string so the operator can reduce the overall costs of deployment. Previously, this was not practical because the tieback string weight could not overcome the wellbore friction in horizontal applications.
In each case, a flotation collar is required to ensure there is enough hook load for deployment of the liner system. The flotation collars used are an interventionless design, utilizing a tempered glass barrier that shatters at a pre-determined applied pressure. The glass debris can be easily circulated through the well without damaging downhole components. This is done commonly on cemented liner and cemented monobore installations, but more rarely with open hole multi-stage completions. For open hole multi-stage completions, the initial installation typically requires an activation tool at the bottom of the well to set the hydraulically activated equipment above.
Multiple validation tests were completed prior to installation by using an activation tool and flotation collar to ensure the debris could be safely circulated through the internals without closing the activation tool. These activation tools have relatively limited flow area and could cause an issue if the glass debris were to accumulate and shift it closed prematurely. Premature closing of the tool would leave expensive drilling fluids in contact with the reservoir, potentially harming production. For the test, the flotation collar was placed only two pup joints away from the activation tool, resulting in a worst-case scenario where a large amount of debris could potentially encounter the internals of the activation tool at one time. In a downhole environment the flotation collar is typically installed near the build or heel of the well, depending on wellbore geometry. The testing was successfully completed, and the activation tool showed no signs of loading. This resulted in a full-scale trial in the field where a 52 stage, open hole (OH) multi-stage fracturing (MSF) liner was deployed using this technology.
Through close collaboration with the operator, an acceptable procedure was established to safely circulate the glass debris and further limit the risk of prematurely closing the activation tool. This paper discusses the OH and cemented MSF deployment challenges, detailed lab testing, and field qualification trials for the single trip deployed system. It also highlights operational procedures and best practices when deploying the system in this fashion. A method to calibrate a torque and drag model will also be explored as part of this discussion.
Rate Transient Analysis of Heterogeneous Unconventional Gas Reservoir Using a Fractional Decline Model
Authors: Luong Loc 
Published: September 2021

Abstract
In this study, an extended version of the fractional decline model is analytically developed for gas flow in fracture reservoir using the anomalous diffusion equation incorporated with the fractional calculus and equation of state. The model can represent the heterogeneity of complex fracture networks and can further be used to interpret reservoir properties by performing type-curve matching of flow rate and cumulative production from multi-fractured horizontal wells in unconventional reservoirs.
To address the limitations of conventional planar fracture idealization, the hydraulic fractures in this present study are integrated with the fracture network, and the fractional diffusivity is solved for a horizontal wellbore. Upon establishing and solving the governing equation in the Laplace domain, the solutions are converted back to the real-time and space domain by performing numerical Laplace inversion. A set of distinctive type curves is generated on the basis of an infinite conductivity horizontal well model, considering early and middle times, in order to capture the heterogeneity of the fractal network in the reservoir model. Application of this new model is demonstrated through type-curve matching of two synthetic cases of simulation data obtained from commercial software; the cases cover orthogonal evenly and unevenly distributed networks. Results from these examples show an acceptable match between the fractional decline model and synthetic data and, hence, showcase the applicability of this model to capture the transient flow in heterogeneous fractured reservoirs.
Improving Well and Reservoir Management Practice Through New Flow Control Philosophy that Prolongs the Life of Production Wells Affected by Water Breakthrough in A Giant Carbonate Oil Field, Abu Dhabi, United Arab Emirates
Authors: Ortiz Requena Jhon Robert , Martinez Santiago Maryvi , Alshehhi Fatmah Mohamed , Daudpota Fareed Ahmad , Fawzy Ahmed Mohamed 
Published: September 2021

Abstract
X Field located in the United Arab Emirates has been developed since 1970's by waterflooding as secondary recovery strategy. As water front advances into oil bank, the well operation practice commonly adopted in many fields for oil wells cutting water has consisted in reducing choke aperture in an attempt to control the water cut trend. However, in wells producing moderate to high water cut, this practice has proven to generate excess water settling in the bottom of the wellbore leading to premature inactivation of the wells.
The reservoir Z in the north of X Field, is a black oil block operated by peripheral and pattern waterflooding. The production wells have been operating by natural lifting since first oil and will continue in natural flow until the Artificial Lift projects are commissioned within a few years. Meanwhile, the field production plateau has been increased arising challenges of production sustainability due to higher risk of acceleration of water breakthrough and consequently higher number of wells becoming inactive earlier. This led to re-assess the Well and Reservoir management strategy to define improved practices oriented to maximize the natural life cycle of wet wells and ensure the compliance of the field production quota. As a result, a new well management approach was devised and adopted to identify and optimize at the earliest stage, wells potentially affected by water loading mismanage. Conceptually, this new practice consisted in comprehensively analyzing well operating conditions, which ultimately generated a flow operating window that improved the multiphase flow performance in wellbores, minimized water slippage avoiding it to settle down and its associated problems, whilst respecting the compliance of technical guidelines for optimum reservoir management.
Based on observations and data gathered from portable testing jobs, saturation logs, PLT and production monitoring; a methodology referred in this work as Critical Flow Analysis, has been successfully implemented in several naturally flowing wells with water cuts ranging from 15 – 40 % in Reservoir Z in X Field, which resulted in prolonged natural life, extra oil recovered, and avoided the negative impact of inactive string count on the Field Management KPI.
The Critical Flow analysis has been a comprehensive well management evaluation and operation philosophy in Reservoir Z which helped to manage more efficiently and in cost-saving fashion the performance of oil wells located in high risk areas, in addition to contribute with stablishing best practices for well and reservoir management that could be extended to analog fields in the area.
A Real-Time Probabilistic Slide Drilling Dysfunction Advisory to Assist Remote Directional Drilling Operations
Authors: Ramos Dawson , Ashok Pradeepkumar , Yi Michael , D’ Angelo John , Rostagno Ian , Bohlander Spencer , Thetford Taylor , Moisan James , Behounek Michael , Noworyta Mickey , Beasley Jason , Wilson Joshua 
Published: September 2021

Abstract
Current slide drilling practices rely heavily on the intuition of the directional drillers to identify and correct drilling dysfunctions. Monitoring numerous dysfunctions simultaneously requires more complex analysis than can be done manually in real-time. There is also currently a big shift towards remote directional drilling. And as such, there is the need for a tool that can, in real-time, diagnose slide drilling dysfunctions accurately and provide advisory to both the remote directional drillers and rig crew.
This paper proposes a method for a real-time slide drilling advisory system consisting of a probabilistic model which computes the likelihood that various slide drilling dysfunctions are occurring and an algorithm that determines what corrective action, if any, should be taken as a result. The dysfunction types monitored include buckling, high friction, poor toolface control, stick slip, and bit bounce. The model employs a Bayesian network which uses evidence derived from transient drilling data trends to infer the probability that any of the five considered dysfunctions are taking place. Data trends known to correlate with each dysfunction type are considered simultaneously to ensure that all dysfunction types are monitored continuously. As dysfunction probabilities are calculated, the algorithm cross references them with current drilling parameters and contextual data to determine necessary corrective actions. Corrective actions are output in the form of simple drilling parameter changes shown on a customizable graphical display.
The dysfunction beliefs calculated were validated using historical data gathered from North America land drilling operations. For high friction and poor toolface control, known instances of dysfunction were identified using information in drilling logs and expert opinion and used for validation. The validation process resulted in a further refinement of the model. The proposed model along with graphical advisory displays were deployed on rigs in several North American land well drilling operations, as well as in the remote directional drilling center.
While there is a lot of prior work that enables identification of rotary drilling dysfunctions in real-time, this is the first method that diagnoses slide drilling dysfunctions in real-time. The approach combines physics based models with a Bayesian network to improve accuracy and robustness in dysfunction detection. Additionally, it considers both real-time drilling data as well as drilling data from the past when diagnosing dysfunctions and facilitates remote directional drilling.
Automating Well Log Correlation Workflow Using Soft Attention Convolutional Neural Networks
Authors: Abubakar Aria , Kulkarni Mandar , Kaul Anisha 
Published: September 2021

Abstract
In the process of deriving the reservoir petrophysical properties of a basin, identifying the pay capability of wells by interpreting various geological formations is key. Currently, this process is facilitated and preceded by well log correlation, which involves petrophysicists and geologists examining multiple raw log measurements for the well in question, indicating geological markers of formation changes and correlating them with those of neighboring wells. As it may seem, this activity of picking markers of a well is performed manually and the process of ‘examining’ may be highly subjective, thus, prone to inconsistencies. In our work, we propose to automate the well correlation workflow by using a Soft- Attention Convolutional Neural Network to predict well markers. The machine learning algorithm is supervised by examples of manual marker picks and their corresponding occurrence in logs such as gamma-ray, resistivity and density. Our experiments have shown that, specifically, the attention mechanism allows the Convolutional Neural Network to look at relevant features or patterns in the log measurements that suggest a change in formation, making the machine learning model highly precise.
Exploration Drilling Management System Based on Digital Twins Technology
Authors: Kalinin Oleg , Elfimov Maksim , Baybolov Timur 
Published: September 2021

Abstract
Digital transformation of oil and gas companies requires consistent improvement of work performance management. Oil and gas companies strive to improve work efficiency and consistently develop and implement digital products. The realization of such complicated solutions requires deep diving into current business processes and transformation of them. This paper deals with implementation of digital management system for exploration and production wells.
Digital management system for exploration and production wells is based on ideology of digital twin and act as a single window and single source of data for all exploration and production wells. Digital management system covers whole construction process started from planning stage to execution and results assessment and orchestrates the exchange of data between process phases and people involved in it. Transparency provided by the digital twin improves efficiency and accelerates well construction process.
Cognitive assistants based on AI and ML techniques are implemented at every stage: while planning, the assistants search analogue wells, analyze its design and complications while drilling and provide recommendations for the most optimal well design, offers the optimum drilling mud density and recommends the most suitable set of logs to cover geological section uncertainty. At the execution stage, a number of ML assistants are used to increase efficiency and reduce risks while drilling: automatic method for anomaly detection while drilling to prevent complications while drilling, machine learning based model for automatic torque and drag control to control borehole condition to predict any signs of differential stuck, key sitting and pack-off, data-driven model for drilling bit position and direction determination to predict BHA position while drilling including a blind zone, data-driven model for the identification of the rock type at a drilling bit for correct geosteering application.
Downhole Oil Water Separation to Handle Produced Water Study Case Onshore & Offshore Fields Abu Dhabi
Authors: Ladmia Abdelhak , Alblooshi Younes , Alobedli Abdullah , Zivanov Dragoljub , Kuliyev Myrat , Deblais Eric , Al Beshr Manal , Al Shmakhy Ahmed , Abdullsalam Fouad , El Bekshy Amer , Almarzooqi Maitha , Maulana Sahid , Bin Ali Amirul , Douik Hela , Al Jaberi Bashaer , Abdelkerim Ali Issa 
Published: September 2021

Abstract
The expected profiles of the water produced from the mature ADNOC fields in the coming years imply a 5-fold increase and the OPEX of the produced / injected water will increase considerably. This requires in-situ water separation and reinjection. The objective is to reduce the cost of handling produced water and to extend the well natural flow performance resulting in increased and accelerated production.
The current practice of handling produced water is inexpensive in the short term, but it can affect the operating cost and the recovery in the long term as the expected water cut for the next 10-15 years is high.
A new water management tool called downhole separation technology was developed. It separates Oil & Gas from produced water inside the wellbore and injects the produced water into the disposal wells. The Downhole Oil Water Separation Technology is one of the key development strategies that will reduce the handling Produced water, improve the recovery, and minimize field development cost by eliminating surface water treatment and disposal well.
The main benefits for DHOWS are to accelerate Oil Offtake, reduce Production Cost, Lower Water Production and Improve facility Utilization. DHOWS require specific criteria to meet the objectives of the well. Multi-disciplined inputs are needed to properly install effective DHOWS, but robust design often brings strong performance.
This paper describes the fundamental criteria and workflow for selecting the most suitable DHOWS design for new and sidetracked wells to deliver ADNOC production mandates cost effectively while meeting completion requirements and adhering to reservoir management guidelines.