bluuebunny/crossref_metadata_2025_split huggingface dataset:
Recent Case Histories of Multilateral Systems Enabling Thru Tubing Intervention in the Middle East
Authors: Butler Benjamin , Kelsey Matthew , Racine Baidy 
Published: December 2021

Abstract
Historically, the ability to perform intervention on multilateral wells has been limited. A lack of technologies pre 2010 with sufficient case histories and life of well reliability meant that generally multilateral well architecture was not selected in applications where thru tubing intervention was a requirement. In recent years, multilateral well architecture has continued to increase in demand, with more ML wells drilled and completed in the last five years than any other five-year period in the technology’s history. With this increased demand has come industry enthusiasm to further mature its intervention capabilities.
This paper will review two recent case histories of separate multilateral well completion systems that enable intervention. This opens up new potential for industry to take advantage of the cost reductions achieved with multilaterals in a much larger scope of well applications.
Two separate completion systems will be covered, system A in this paper installed in a cemented multilateral junction, and system B, a completion that creates a hydraulically isolated junction via a either a dual string completion or a single string completion that splits into two strings. These case histories were exectured in 2017 to 2019, and interventions were performed after one to two years of production.
Detailed in each case study will be an overview of the equipment, the operational sequence, intervention outcome, and any lessons learned or improvements.
The systems have demonstrated themselves a reliable method to access laterals in non-ideal downhole environments where debris is present after the well has been on production. The tubing sizes for the case studies is 3-1/2" and 4-1/2". In each of these wells, the following operations have been successfully performed: drift testing, acid stimulation through coil tubing and breaking of a ceramic disc. Both slickline and coil tubing have been used for the interventions and in some cases with tractors. Junction inclinations range from 1 to 43 degrees.
Plans for ongoing installations for the systems are being executed in the Middle East Region. Further, expansion of the system A capabilities by integrating it with other existing technologies is also planned. This will enable projects such as the installation of a trilateral well with flow control and intervention for each individual leg, and also the conversion of existing single bore wells to multilateral with intervention capability.
Transformer-Based Deep Learning Models for Well Log Processing and Quality Control by Modelling Global Dependence of the Complex Sequences
Authors: Kumar Ashutosh 
Published: December 2021

AbstractA single well from any mature field produces approximately 1.7 million Measurement While Drilling (MWD) data points. We either use cross-correlation and covariance measurement, or Long Short-Term Memory (LSTM) based Deep Learning algorithms to diagnose long sequences of extremely noisy data. LSTM's context size of 200 tokens barely accounts for the entire depth. Proposed work develops application of Transformer-based Deep Learning algorithm to diagnose and predict events in complex sequences of well-log data.Sequential models learn geological patterns and petrophysical trends to detect events across depths of well-log data. However, vanishing gradients, exploding gradients and the limits of convolutional filters, limit the diagnosis of ultra-deep wells in complex subsurface information. Vast number of operations required to detect events between two subsurface points at large separation limits them. Transformers-based Models (TbMs) rely on non-sequential modelling that uses self-attention to relate information from different positions in the sequence of well-log, allowing to create an end-to-end, non-sequential, parallel memory network. We use approximately 21 million data points from 21 wells of Volve for the experiment.LSTMs, in addition to auto-regression (AR), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) conventionally models the events in the time-series well-logs. However, complex global dependencies to detect events in heterogeneous subsurface are challenging for these sequence models. In the presented work we begin with one meter depth of data from Volve, an oil-field in the North Sea, and then proceed up to 1000 meters. Initially LSTMs and ARIMA models were acceptable, as depth increased beyond a few 100 meters their diagnosis started underperforming and a new methodology was required. TbMs have already outperformed several models in large sequences modelling for natural language processing tasks, thus they are very promising to model well-log data with very large depth separation. We scale features and labels according to the maximum and minimum value present in the training dataset and then use the sliding window to get training and evaluation data pairs from well-logs. Additional subsurface features were able to encode some information in the conventional sequential models, but the result did not compare significantly with the TbMs. TbMs achieved Root Mean Square Error of 0.27 on scale of (0-1) while diagnosing the depth up to 5000 meters.This is the first paper to show successful application of Transformer-based deep learning models for well-log diagnosis. Presented model uses a self-attention mechanism to learn complex dependencies and non-linear events from the well-log data. Moreover, the experimental setting discussed in the paper will act as a generalized framework for data from ultra-deep wells and their extremely heterogeneous subsurface environment.
Case Study of a Novel Autonomous Real-Time Monitoring, Control and Analysis System, to Maximize Production Uptime on Sustained Annulus Pressure Wells, While Improving HSE and Compliance with Double Barrier Well Integrity Policies
Authors: Dsouza Rylan Paul , Cornwall Rachelle Christine , Brodie Alan David , Patela Pedro , Daghmouni Hamdi Bouali , Arakkalakkam Mohammad Hariz , Boni Venkata Praveen Kumar , Khan Asif Khan Haq Dad 
Published: December 2021

Abstract
This paper describes an innovative solution for the safe and effective management of wells with unplanned sustained annulus pressure (SAP). The solution restores double barrier integrity in the well and provides reliable real time annulus pressure and temperature data. It also has the functionality to autonomously bleed-off the annulus pressure at a pre-determined set point. As a result, the nature and severity of the SAP can be better understood, and in many cases wells that would otherwise have been closed in awaiting workover can remain in production.
Intelligent Well Drainage and Interference Density Analysis Tool for Optimizing Infill Drilling
Authors: Al-Sane Amal , Al-Bahar Mohammad A. , Bora Anup , Dhote Prashant S. , Nalla Gopi , Walker Jacob G. , Caicedo Vladimir 
Published: December 2021

Abstract
During the progressive development of mature fields, it is imperative to drill many infill wells to accelerate production and access bypassed oil. Optimizing the infill well spacing is always the concern to reduce interference with existing wells and improve recovery. In the present study, using intelligent data mining techniques, a new analysis and visualization tool has been developed and implemented to estimate and map drainage radius by well to assess the efficiency of the current development pattern and properly plan future wells. The tool deployed several performance-based techniques to estimate the contacted stock-tank oil initially in place (STOIIP) by each existing well, and outcomes can be compared between techniques for validation. The contacted STOIIP is then converted into an effective drainage radius by well using reservoir properties from the geo-cellular model.
The evaluated reservoir is subdivided vertically into pay zones drained by the wells based on geological barriers/baffles to flow and connectivity across the zones. The tool estimates drainage radii of the wells produced from the reservoir using five different methods. The resultant Proved Developed Producing (PDP) reserves polygon maps are generated for the connected zones. The drainage radii of wells with behind-casing opportunities are estimated based on correlation and adjacent wells methods, and Proved Developed Non-Producing (PDNP) reserves polygon maps were generated. Well interference density is estimated based on overlapping drainage radii polygons with adjacent well locations, which has then been validated with production and pressure data from the wells. This paper describes the methodology by which the well drainage radii and well interference density can be estimated and implemented on a selected reservoir.
This workflow can be successfully used to identify drained and undrained areas around the wellbore and opportunities for additional infill wells in the various pay zones of the reservoir. This exercise observed consistency in the drainage radii cumulative distribution from decline curve analysis methods and the No-Further-Activities (NFA) simulation case.
A Critical Literature Review on Rock Petrophysical Properties Estimation from Images Based on Direct Simulation and Machine Learning Techniques
Authors: Rizk Ahmed Samir , Tembely Moussa , AlAmeri Waleed , Al-Shalabi Emad W. 
Published: December 2021

Abstract
Estimation of petrophysical properties is essential for accurate reservoir predictions. In recent years, extensive work has been dedicated into training different machine-learning (ML) models to predict petrophysical properties of digital rock using dry rock images along with data from single-phase direct simulations, such as lattice Boltzmann method (LBM) and finite volume method (FVM). The objective of this paper is to present a comprehensive literature review on petrophysical properties estimation from dry rock images using different ML workflows and direct simulation methods. The review provides detailed comparison between different ML algorithms that have been used in the literature to estimate porosity, permeability, tortuosity, and effective diffusivity. In this paper, various ML workflows from the literature are screened and compared in terms of the training data set, the testing data set, the extracted features, the algorithms employed as well as their accuracy. A thorough description of the most commonly used algorithms is also provided to better understand the functionality of these algorithms to encode the relationship between the rock images and their respective petrophysical properties.
The review of various ML workflows for estimating rock petrophysical properties from dry images shows that models trained using features extracted from the image (physics-informed models) outperformed models trained on the dry images directly. In addition, certain tree-based ML algorithms, such as random forest, gradient boosting, and extreme gradient boosting can produce accurate predictions that are comparable to deep learning algorithms such as deep neural networks (DNNs) and convolutional neural networks (CNNs). To the best of our knowledge, this is the first work dedicated to exploring and comparing between different ML frameworks that have recently been used to accurately and efficiently estimate rock petrophysical properties from images. This work will enable other researchers to have a broad understanding about the topic and help in developing new ML workflows or further modifying exiting ones in order to improve the characterization of rock properties. Also, this comparison represents a guide to understand the performance and applicability of different ML algorithms. Moreover, the review helps the researchers in this area to cope with digital innovations in porous media characterization in this fourth industrial age – oil and gas 4.0.
Indications of Deep Marine Fans in the Early Miocene Foredeep of Lower Austria: A Potential New Play
Authors: Hinsch Ralph 
Published: December 2021

AbstractThe petroleum province in Lower Austria resulted from the Alpine collision and the subsequent formation of the Vienna Basin. OMV is active in this area since its foundation in 1956. Several plays have been successfully tested and produced in this complex geological region. The main exploration focus is currently on the deep plays. However, this paper proposes a so far unrecognized and therefore undrilled play in a shallower level to broaden OMV's portfolio in Austria.Seismic re-interpretations of reprocessed 3D seismic data and structural reconstructions were used to review some of the existing plays and get novel ideas from improved understanding of processes. In the frontal accretion zone of the Alpine wedge, the Waschberg-Ždánice zone discoveries are limited to the frontal thrust unit and associated structures. The more internal parts of the thrust belt have only sparsely been drilled and are perceived not to have high-quality reservoir rocks.The detailed structural interpretations indicated that the foredeep axis during the Early Miocene was positioned in the thrust sheet located directly in front of the advancing Alpine wedge (comprising the eroding Rhenodanubian Flysch in its frontal part). Seismic amplitude anomalies can be interpreted to represent Lower Miocene basin floor and slope fans. Nearby wells did not penetrate these fans but drilled instead shale-dominated lithologies. Thus, the presence of potential sand-rich fans in front of the advancing alpine wedge is considered a potential new play in Lower Austria. Analogues are found in Upper Austria some 250 km to the West, where several large gas fields in Lower Miocene deposits located in front of the advancing Alpine wedge have been discovered by another operator. In that area the fans are only partly involved in the fold-thrust belt. In Lower Austria, these fans are located within the rear thrust sheet(s), providing a structural component to a mixed structural-stratigraphic trap. Two potential charge mechanism can be considered: a) biogenic gas charge from the organic matter of surrounding shales (like the Upper Austria analogues) or b) oil charge via the thrust fault planes from the Jurassic Mikulov Formation (the proven main source rock in the broader area).Our results add to the understanding of the Miocene structural-stratigraphic evolution of the Alpine collision zone. The definition of a potential new play may add significant value to OMV's upstream efforts in a very mature hydrocarbon province.
Reconstruction of Missing Segments in Well Data History Using Data Analytics
Authors: Li Yuanjun , Horne Roland , Al Shmakhy Ahmed , Felix Menchaca Tania 
Published: December 2021

Abstract
The problem of missing data is a frequent occurrence in well production history records. Due to network outage, facility maintenance or equipment failure, the time series production data measured from surface and downhole gauges can be intermittent. The fragmentary data are an obstacle for reservoir management. The incomplete dataset is commonly simplified by omitting all observations with missing values, which will lead to significant information loss. Thus, to fill the missing data gaps, in this study, we developed and tested several missing data imputation approaches using machine learning and deep learning methods.
Traditional data imputation methods such as interpolation and counting most frequent values can introduce bias to the data as the correlations between features are not considered. Thus, in this study, we investigated several multivariate imputation algorithms that use the entire set of available data streams to estimate the missing values. The methods use a full suite of well measurements, including wellhead and downhole pressures, oil, water and gas flow rates, surface and downhole temperatures, choke settings, etc. Any parameter that has gaps in its recorded history can be imputed from the other available data streams.
The models were tested on both synthetic and real datasets from operating Norwegian and Abu Dhabi reservoirs. Based on the characteristics of the field data, we introduced different types of continuous missing distributions, which are the combinations of single-multiple missing sections in a long-short time span, to the complete dataset. We observed that as the missing time span expands, the stability of the more successful methods can be kept to a threshold of 30% of the entire dataset. In addition, for a single missing section over a shorter period, which could represent a weather perturbation, most methods we tried were able to achieve high imputation accuracy. In the case of multiple missing sections over a longer time span, which is typical of gauge failures, other methods were better candidates to capture the overall correlation in the multivariate dataset.
Most missing data problems addressed in our industry focus on single feature imputation. In this study, we developed an efficient procedure that enables fast reconstruction of the entire production dataset with multiple missing sections in different variables. Ultimately, the complete information can support the reservoir history matching process, production allocation, and develop models for reservoir performance prediction.
Core-to-Field-Scale Simulations of Polymer Viscoelastic Effect on Oil Recovery Using the Extended Viscoelastic Model
Authors: Zeynalli Mursal , Al-Shalabi Emad W. , AlAmeri Waleed 
Published: December 2021

AbstractBeing one of the most commonly used chemical EOR methods, polymer flooding can substantially improve both macroscopic and microscopic recovery efficiencies by sweeping bypassed oil and mobilizing residual oil, respectively. However, a proper estimation of incremental oil to polymer flooding requires an accurate prediction of the complex rheological response of polymers. In this paper, a novel viscoelastic model that comprehensively analyzes the polymer rheology in porous media is used in a reservoir simulator to predict the recovery efficiency to polymer flooding at both core- and field-scales. The extended viscoelastic model can capture polymer Newtonian and non-Newtonian behavior, as well as mechanical degradation that may take place at ultimate shear rates. The rheological model was implemented in an open- source reservoir simulator. In addition, the effect of polymer viscoelasticity on displacement efficiency was also captured through trapping number. The calculation of trapping number and corresponding residual-phase saturation was verified against a commercial simulator.Core-scale tertiary polymer flooding predictions revealed the positive effect of injection rate and polymer concentration on oil displacement efficiency. It was found that high polymer concentration (&gt;2000 ppm) is needed to displace residual oil at reservoir rate as opposed to near injector well rate. On the other hand, field-scale predictions of polymer flooding were performed in a quarter 5-spot well pattern, using rock and fluid properties representing the Middle East carbonate reservoirs. The field-simulation studies showed that tertiary polymer flooding might improve both volumetric sweep efficiency and displacement efficiency. For this case study, incremental oil recovery by polymer flooding is estimated at around 11 %OOIP, which includes about 4 %OOIP residual oil mobilized by viscoelastic polymers. Furthermore, the effect of different parameters on the polymer flooding efficiency was investigated through sensitivity analysis. This study provides more insight into the robustness of the extended viscoelastic model as well as its effect on polymer injectivity and related oil recovery at both core- and field-scales. The proposed polymer viscoelastic model can be easily implemented into any commercial reservoir simulator for representative field-scale predictions of polymer flooding.
Age Doesn't Matter: Digitizing the as-is Condition of Brownfield Assets is Part of a Leaner & Greener Future
Authors: Tedstone Lee 
Published: December 2021

Abstract
The energy sector is facing more challenges than ever before – from the impacts of the pandemic on already low margins, to Environmental, Social and Governance pressures mounting to gain investment and attract skilled workers in a competitive market. Over past year, the oil and gas sector has accelerated a critical path to digital transformation, paving its way to a more agile, greener future.
Data-sharing and collaboration enabled through the Digital Twin helps companies drive efficiency, which in turn creates opportunities to improve sustainability performance, even on legacy assets. This presentation will demonstrate how moving to Digital Twin technology is a viable brownfield solution to many of the industries pressing challenges today. It will also make the business case for how digitalization and digital twin connectivity are not only possible on a brownfield asset but also, why it is critical to meet the needs of the plant of the future.
Underwater Inspection Using ROV
Authors: Elshahawy Mahmoud Ahmed , Younes Helmy Abdel Wahab , Al Hamlawi Imad 
Published: December 2021

Abstract
ADNOC Drilling operates a growing fleet of 22 jack up units. These units require various inspections and tests to ensure that their integrity is maintained while conducting the drilling operations. One of these inspections is the underwater inspection which is required to be carried out twice every 5 years. Traditionally, this inspection is carried out by divers at the shipyard where it is safe for divers to carry out cleaning, visual inspections and NDT of structural welds.
Moving the rig to a drydock or a shipyard is a costly and involves a lot of activities related to safety in addition to the out of service time. Loss of revenue is experienced while the rig is out of service, as well as costs associated to the survey, shipyard costs, vessel costs etc. all combining to create an expensive inspection process.
ADNOC Drilling Marine and Group Technology adapted a new method for performing the full scope of the underwater inspection offshore using small remotely operated vehicles (ROV), most of the scope is carried out while the rig remains in full operation (while drilling).
Promotive Selectivity to C2-C3 Polyols From Alkaline Cellulose Hydrogenated by Ionic Liquid-stablized Ru Nanoparticles
Authors: Liu Mingrui , Wang Hua 
Published: December 2021

Abstract
Alkaline cellulose hydrogenolysis on metal catalyst was an effective way to get C2~C3 polyols. The alkaline cellulose was obtained by treating cellulose with 4 wt% NaOH solution. Ionic liquid-stablized Ru nanoparticles were prepared by reducing metal salt in ionic liquid. The SEM results indicate that the amorphous part of alkaline cellulose is helpful for getting the catalyst into the cavities to have a further hydrogenation reaction. When hydrogenolysis of alkaline cellulose over Ru/[Bmim]BF4 nanoparticles was conducted at 433 K, 63.78% of the substrate was converted with glycerol, 1,2-propanediol and ethylene glycol as main products of which selectivity was up to 58.91 %, whereas the conversion rate over Ru/C catalyst of alkaline cellulose was 59.23 % and only 26.11 % C2~C3 polyols were detected. Moreover, if the ionic liquid-stablized Ru nanoparticles were doped with 53.7 % Ni, the selectivity of C2~C3 polyols was promoted to 65.07 %. These results suggested the advantages of the ionic liquid-stablized Ru nanoparticles, especially doping with Ni, have potentials for promotive selectivity to C2~C3 alcohols. Put forward the plausible mechanism finally.