bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-Class Taxonomy of Well Integrity Anomalies Applying Inductive Learning Algorithms: Analytical Approach for Artificial-Lift Wells
Authors: Yakoot Mostafa Sa'eed , Ragab Adel Mohamed Salem , Mahmoud Omar 
Published: September 2021

Abstract
Well integrity has become a crucial field with increased focus and being published intensively in industry researches. It is important to maintain the integrity of the individual well to ensure that wells operate as expected for their designated life (or higher) with all risks kept as low as reasonably practicable, or as specified. Machine learning (ML) and artificial intelligence (AI) models are used intensively in oil and gas industry nowadays. ML concept is based on powerful algorithms and robust database. Developing an efficient classification model for well integrity (WI) anomalies is now feasible because of having enormous number of well failures and well barrier integrity tests, and analyses in the database.
Circa 9000 dataset points were collected from WI tests performed for 800 wells in Gulf of Suez, Egypt for almost 10 years. Moreover, those data have been quality-controlled and quality-assured by experienced engineers. The data contain different forms of WI failures. The contributing parameter set includes a total of 23 barrier elements.
Data were structured and fed into 11 different ML algorithms to build an automated systematic tool for calculating imposed risk category of any well. Comparison analysis for the deployed models was performed to infer the best predictive model that can be relied on. 11 models include both supervised and ensemble learning algorithms such as random forest, support vector machine (SVM), decision tree and scalable boosting techniques. Out of 11 models, the results showed that extreme gradient boosting (XGB), categorical boosting (CatBoost), and decision tree are the most reliable algorithms. Moreover, novel evaluation metrics for confusion matrix of each model have been introduced to overcome the problem of existing metrics which don't consider domain knowledge during model evaluation.
The innovated model will help to utilize company resources efficiently and dedicate personnel efforts to wells with the high-risk. As a result, progressive improvements on business, safety, environment, and performance of the business. This paper would be a milestone in the design and creation of the Well Integrity Database Management Program through the combination of integrity and ML.
Advanced Cement Mechanical Integrity for Thermal Wells
Authors: Vu Manh-Huyen , Bouhours Aurélien , Bouhours Julien , Bouchair Rym , Bois Axel-Pierre , Badalamenti Anthony 
Published: September 2021

Abstract
Ensuring wells’ cement mechanical integrity (CMI) is of paramount importance for the success of a thermal project. Failed cement sheaths can lead to loss of production, environmental pollutions, or even to well abandonment. Over time, CMI software applications have been developed to design wells that do not leak. However, their efficiency depends not only on if their equations are verified, but also on how the models are validated versus wells’ downhole conditions. Unfortunately, most CMI tool designers have focused on only verifying if the models are mathematically correct, checking what is the time required for a simulation, and improving how are the simulations reported to the user. Typically, little time is dedicated on validating that the correct model is used for the specific well. This foresight has led to non-predictive CMI tools, which do not allow optimizing well designs.
The authors have been involved for more than 15 years in developing and validating CMI models. They have shown the importance of simulating the cement hydration to evaluate the state of stress in the cement after it has set. They also have highlighted how the plastic behavior of the cement design can lead to opening micro-annuli at the cement-sheath's interfaces. Recently the authors have started theoretical work in the area of the cement integrity of high and ultra-high temperature wells and how these temperatures, either naturally occurring or induced, could affect the cement's mechanical integrity. The work has focused on modeling the increase in pore pressures, the opening of micro-annuli at the cement sheath's boundaries, and the phase changes which take place in the cement when it is heated to high temperature values. To date this work showed that heating cement up to 250°C can result in pore pressures larger than 100 MPa unless if the pore pressures can be released. This work has also identified three mechanisms that can lead to such release of pore pressures: 1) During cement hydration, due to the water consumption by the chemical reactions, 2) When a micro-annulus opens due to the large pore pressures, therefore allowing venting the pressures to the surface or to a downhole reservoir, and 3) When a change of phase occurs in the cement when heated to more than 110°C, as this leads to the creation of additional porosity in the cement. All this means that the cement sheath should not be simulated as a closed system, but rather as an open thermo-hydro-chemo-mechanics. How these features impact CMI has never been studied before even if they can explain why some cement designs lead to tight cement sheath and other to leaking ones. This paper highlights the work that has been done and when these conditions should be considered, and if it is feasible to design cement sheaths that do not fail, even at very high temperatures.
Automated Verification of Sidewall Core Recovery Depth using Borehole Image Logs
Authors: Al Ibrahim Mustafa A , Torlov Vladislav , Mezghani Mokhles M 
Published: September 2021

Abstract
Sidewall coring is a cost-effective process to complement conventional fullbore coring. Because sidewall cores target exact depth points, verification of the sidewall core recovery depth is required. We present an automated, fast workflow to perform the depth verification using borehole images, thereby providing consistent results. An application example using a typical dataset is used to showcase the workflow. A novel automated approach based on image analysis techniques and Bayesian statistical analysis is developed to verify sidewall core recovery depth using borehole image logs. A complete workflow is presented covering: 1) utilization of reference logs, e.g., gamma ray, to correct image log depth using cross correlation and/or dynamic time warping, 2) automated identification of sidewall core cavity in borehole image log using the circle Hough transform, and 3) estimation of confidence in the identification using Bayesian statistics and specialized metrics. The workflow is applied on a typical dataset containing tens of sidewall core cavities with varying quality. Results are comparable to the manual interpretation from an experienced engineer. A number of observations are made. First, the use of reference logs to correct the image log allows for determining the exact well logs values where the sidewall core was sampled, which is then compared to the initial target well logs values. This increases the confidence that the target lithofacies was sampled as planned. Second, the circle Hough Transform is suitable for this problem because it provides stable solutions for partially imaged sidewall core cavities typical in pad-based borehole images. Third, the use of Bayesian statistics and specialized metrics for the problem, such as average and standard deviation borehole image intensity in the cavity, provides customizability to work with multiple types of borehole images and with varying initial depth guess uncertainties. Overall, the use of fast and automated methodology for depth verification opens up avenues for near real-time combined sidewall coring, imaging, and verification workflows. The novelty in this study lies in using a combination of image processing techniques and statistical analysis to automate an established manual workflow. The automated workflow provides consistent results in minutes rather than hours. Results also incorporate a confidence index estimation.
Mangala Polymer Flood Performance: Connecting the Dots Through in Situ Polymer Sampling
Authors: Shankar Vivek , Sunit Shekhar , Brown Alasdair , Kumar Gupta Abhishek 
Published: September 2021

Abstract
The paper describes the in-situ polymer sampling in Mangala which helped explain the performance of a large polymer flood in Mangala field in India.
The Mangala field contains medium-gravity viscous crude oil. Notably, it is the largest polymer flood in India and 34% of the STOIIP has been produced in 11 years of production. Mangala was put on full field polymer flood in 2015, six years after the start of field production on water flood in 2009. Polymer flood added 93 million barrels above the anticipated water flood recovery in 6 years. Reservoir simulation models could replicate the initial Mangala polymer flood performance. However, the performance of the lower layers of Mangala (FM-3 and FM-4) continued to progressively deviate from modeling estimates. Equally importantly, the prediction of polymer breakthrough deviated significantly from modeling estimates.
After 6 years and 0.7 pore volumes of polymer injection, it is apparent that field performance is equivalent to only 50-60% of the viscosity of the polymer injected at the surface. To better understand and quantify the nature and extent of polymer degradation it is necessary to gather representative down hole samples of polymer which has stayed in the reservoir conditions for a considerable length of time. Accelerated ageing studies in the lab showed HPAM can lose viscosity and precipitate after prolonged exposure to Mangala reservoir conditions with an increase in the degree of hydrolysis as the primary reason for the degradation. The concept of transfer function based on first order kinetics was used to extrapolate the laboratory results to Mangala reservoir temperatures. To test the hypothesis, a multi-disciplinary team implemented a plan to gather a representative polymer sample from the reservoir. The polymer sample had been in the reservoir for nearly 120 days and was captured in low shear and anaerobic conditions to minimize shear and oxidative degradation.
The sample was tested for degree of hydrolysis by NMR method. The results confirmed that the level of hydrolysis of the injected HPAM did increase in the reservoir leading to lower viscosity and reduced lower amide concentration. Preliminary simulation studies using the concept of viscosity half-life were used to mimic the polymer degradation with time in the reservoir. The method is quite a simplistic representation of the thermal degradation, but it significantly improved the model's water cut predictions for lower layers and the full field polymer breakthrough predictions. The impact of polymer precipitation in the reservoir on the permeability is under study and it will drive the next phase of more detailed modeling.
A Predictive Model to Detect the Impending Electric Submersible Pump Trips and Failures
Authors: Peng Long , Han Guoqing , Pagou Arnold Landjobo , Zhu Liying , Ma He , Wu Jiayi , Chai Xiaolong 
Published: September 2021

Abstract
Trips and failures are common occurrences in the Electric Submersible Pump (ESP) systems. The random nature of these trips and failures will lead to low industry run-life and high workover costs for ESP companies and operators. To perform early detection and take corrective actions to handle the potential incidents, ESP operation data collected from downhole and surface sensors are used to perform diagnostics and prognostics to identify trips and failures. In this study, Principal Component Analysis (PCA) method serves as a pre-processing method to retain the most essential principal components to reevaluate the initial ESP system. For a single well system, the Squared Prediction Error (SPE) and Hotelling T-square statistic (T2) equations are employed for numerical visualization in the new principal component space and therefore detection of the potential ESP trips or failures. For the whole well group, the score plot of three principal components provides a solution that enables to distinguish different clusters of stable operation, trip and failure regions, and diagnose the upcoming ESP trips and failures. In this way, the predictive model is bulit to continuously analyze the ESP operation and automatically perform health monitoring for any ESP system. This paper concludes that the predictive model has the potential to construct a real-time proactive surveillance system to identify dynamic anomalies and therefore predict developing trips or failures in the ESP system.
Is Decline Curve Analysis the Right Tool for Production Forecasting in Unconventional Reservoirs?
Authors: Molina Oscar , Santos Laura , Herrero Francisco , Monaco Agustin , Schultz Darren 
Published: September 2021

Abstract
This study presents a novel metaheuristic algorithm that uses a physics-based model for multi-fractured horizontal wells (MFHW) to accurately predict the estimated ultimate recovery (EUR) for unconventional reservoirs. The metaheuristic algorithm creates a sizeable number of stochastic simulations and keeps the simulation results from those random models that closely reproduce observed production data. Unlike other optimization methods, the proposed algorithm does not aim at finding the exact solution to the problem but a group of sufficiently accurate solutions that help to construct the partial solution to the optimization problem as a function of production history.
Results from this work provide sufficient evidence as to why traditional decline curve analysis (DCA) is not a suitable solution for production forecasting in unconventional reservoirs. Two case studies are discussed in this work where results from both modeling strategies are compared. Evolutionary prediction of EUR over time using DCA behaves erratically, regardless of the amount of historical production data available to the regression model. Such erratic behavior can, in turn, yield an erroneous estimation of key economic performance indicators of an asset. In contrast, the proposed metaheuristic algorithm delivers precise and accurate results consistently, achieving a significant reduction of uncertainties as more production data becomes available. In conclusion, the proposed partial optimization approach enables the accurate calculation of important metrics for unconventional reservoirs, including production forecasting and expected productive life of an asset.
Understanding AICD Gravel Packing
Authors: Pedroso Carlos , Govinathan Kesavan , Mickelburgh Ian , Wassouf Philip , Latini Carolina 
Published: September 2021

Abstract
In recent years, it has become common practice for operating companies to make a significant effort in the planning of gravel pack installations, especially in their most important wells. Typically, the placement of the gravel pack is accurately modelled, and multiple contingencies developed for potential alternative scenarios to reduce the overall risk of execution. After the pack has been placed, the use of down-hole gauge data enables the gravel pack to be fully evaluated in order to confirm success and investigate any issues or failures. This understanding feeds into improved designs and ever higher success rates for future completions.
The most challenging gravel packs Operators are installing today are those placed in long horizontal open holes, around screens fitted with Inflow Control Devices (ICDs) or Autonomous Inflow Control Devices (AICDs). Simulating gravel pack placement in wells such as these requires the effective modelling of unusually dynamic and complex flow paths. Until recently, no simulator could adequately model these treatments. Most jobs have also been done without the downhole gauges necessary for a complete job evaluation, which has resulted in a lack of data for job evaluation and understanding.
Consequently, completions requiring the pack to be placed around ICD/AICD screen assemblies have, until recently, been done without the assurance of pre-job gravel pack placement modelling. The lack of an adequate simulator has also meant that, even on these complex and valuable wells, Operators have been restricted to simple volumetric evaluation of their success. With no way to understand actual packing mechanisms or investigate root causes of failures, the assumptions made on how packing occurs in these complex wells have remained unconfirmed.
Recent evolution of gravel pack simulators has made the effective pre-job simulation, and post-job evaluation, of gravel packs placed around ICD/AICDs a reality. This paper presents the results of the first evaluation of a multi-proppant deep water horizontal alpha beta gravel pack around AICD screens. It facilitates the understanding of how such gravel packs are placed, validates the packing efficiencies, and illustrates the benefits of using multiple gravels for packing.
Assessment of Radius of Investigation of Pressure Transient Analysis in Highly Heterogeneous Reservoirs
Authors: Zeybek Murat , Jiang Lei , Dumont Hadrien 
Published: September 2021

Abstract
The radius of investigation (ROI) of pressure transient analyses has been traditionally assessed using analytical formulations with basic reservoir parameters for homogenous systems. Numerous studies aimed to improve ROI formulations to incorporate all reservoir and testing parameters such as gauge resolution and rate for more accurate ROI assessments. However, new generation wireline formation testers aim to improve deep transient tests with significant developments in gauge resolution and increasing rate. Challenges still remain in heterogeneous formations such as shaly sands and carbonate reservoirs.
In this study, detailed conceptual high-resolution numerical models are set up, including comprehensive reservoir and measurement parameters, to investigate more realistic ROI assessments in layered heterogeneous systems without and with hydraulic communication. Several conceptual examples are presented in layered systems with permeability contrasts.
In addition, deviation from infinite-acting radial flow (IAFR) and pressure propagation in highly heterogeneous layered systems are investigated to detect the presence of geological features, including closed boundary systems and the presence of a fault in the proximity of wellbore.
Mitigating Scale Deposition by Optimization of Completion Valves Geometry
Authors: Machado Patrick , Carneiro Giovanna , Leibsohn Andre , Bouamra Reda , Handerson Thiago , Franquiz Gabriela 
Published: September 2021

Abstract
The harsh conditions presented in Brazilian presalt, summed up with the complexity of its reservoir, generate a series of challenges to improve reservoir recovery. Routinely, we have used intelligent completion systems to address the major part of the challenges; however, with the new production rates new problems have arrived and the usual ones have turned more aggressive, generating risks even to the intelligent completion systems.
Inorganic scale is a critical challenge in presalt reservoirs production. Future plans for presalt production include more robust flow conditions and the use of an all-electrical completion system. Higher flow rates are likely to increase the risk of scale deposition and an optimum design is required.
To address the new challenges arising from the new perspective of exploration in the presalt fields, we developed the presented workflow to mitigate the scale deposition on completion valves. The method enables the optimization of choke geometry to reduce scale deposition on inflow control valves. The proposed workflow generates a criticalness parameter for geometry classification according to a scenario of mechanical failure (due to sleeve incapacity to move) or deviation of production design point. A computational fluid dynamics (CFD) simulation was developed and benchmarked by experimental data, thus CFD results for different scenarios and various choke geometries were used to build a risk analysis matrix, which allows the definition of the optimal choke design to mitigate scale on ICVs. The extracted criticalness parameter may be used as an evaluator to estimate the time to valve stuck due to scale deposition in a commercial 1D transient flow simulator, optimizing then valve cycling time.
Underbalanced Perforating with 800-ft Guns Completed in Single Trip with Fiber-Optic-Enabled Coiled Tubing: A Case Study from Jambaran Field, East Java, Indonesia
Authors: Maulana Ibnu , Purwanto Bambang , Makriva Doni Arief , Sugiarto Genie Ageng , Kuswardani Diah Setianti , Nuryadin Shakti , Jenie John Rizal , Arthur Parapat 
Published: September 2021

Abstract
Coiled tubing (CT) equipped with fiber optics and real-time downhole telemetry and a fit-for-purpose CT tower were used in underbalanced perforating operations in six wells in Indonesia; each operation involved 800 ft of perforating guns, and each was completed in a single trip. The reservoir is thick, with high permeability and characterized by high content of CO2 and H2S. The underbalanced perforating technique was deemed fundamental to minimize formation damage in the near-wellbore area, and the campaign was part of a national strategic project to develop a block's main reserve to supply gas to drive the national economy.
Each well had to be completed with minimum of an 800-ft perforation interval to deliver an average of 60 MMscf/D gas production for 16 years plateau with up to 34% CO2 content and 10,000-ppm H2S. The traditional method of e-line overbalanced perforating in such harsh environment became inefficient because of the number of runs required, which can be as high as 40 runs per well. CT-conveyed perforating guns and a completion insertion retrieval of equipment under pressure (CIRP) system were chosen to execute the task. The fiber-optic CT real-time telemetry system was selected to improve downhole depth accuracy, confirm the underbalance condition, and provide real-time confirmation when the 800 ft of guns detonated downhole. To execute the six-well campaign safely, a customized 100-ft CT tower was brought into the country.
Because this was the first in-country application for fiber-optic-enabled CT in single-trip with an 800-ft underbalanced perforation interval, thorough planning and preparation were critical for a successful campaign. Considering the high gas rate, high CO2, and H2S content, a downhole lubricator valve was added as additional barrier during undeployment, and an H2S and CO2 inhibitor was used to protect CT string integrity. Another risk mitigation plan was to utilize real-time CT inspection to monitor the CT integrity and condition throughout the job. Slickline deployment was used in first two wells to deploy multiple guns into the well, but this was deemed inefficient. The CT deployment method was used to complete the campaign. The project comprised a total of 2,200 operating hours, 29 CT runs, and 4,969 ft of guns in six trips with 917 ft as the longest interval. All six wells were completed with no HSE events, no automotive incidents, 98% operational efficiency, and 21% faster than planned duration.
This successful six-well campaign represents a first in-country application, which contributed to developing this main gas reserve. The campaign provides lessons for job planning and preparation, technology implementation, execution, and continuous improvement, which can be implemented in similar projects in Indonesia and around the region.