bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Experimental Study on Shale-Fluid Interactions to Help Drill Stable Shale Formations
Authors: Al-Arfaj Mohammed K. 
Published: February 2022

AbstracDrilling stable boreholes in shale formations is a challenging task due to the potential interactions of clay-rich shales with aqueous phase of drilling fluids. The objective of this paper is to look into some testing techniques utilized to characterize shale rock samples and assess the reactivity, recommend a strategy and best practices to overcome the wellbore instability problems when drilling shale formations.Wellbore instability associated with drilling shale formations is a major source of non-productive time in drilling operations. Inhibiting shale-fluid interactions and minimizing the scope of time-dependent shale problems are essential to mitigate this challenge. The solution is of two-fold: mechanical and chemical. This paper addresses the chemical aspects of the problem and recommends a novel type and combination of shale inhibitors to use in drilling.Different testing techniques were employed to assess the shale reactivity with different types of drilling fluids. After characterizing the shale samples in terms of mineralogy, petrography and inhibition testing has been carried out to reveal the reactivity extent of the shales. Dispersion test predict the shale-fluid interactions to help in optimizing the drilling fluid formulations to drill those shale formations. Two shale inhibitor combinations were tested against shale samples with different characteristics and clay mineralogy to develop a semi-universal type of inhibitive drilling fluid formulation.
A New Approach to Quantify the Wellhead Performance for Gas Condensate Reservoirs Using Artificial Intelligent Techniques
Authors: Eliebid Mohammed , Hassan Amjed , Mahmoud Mohamed , Abdulraheem Abdulazeez 
Published: February 2022

Abstract
Selecting the optimum choke size that can deliver the optimum gas flow rate is extremely complex in gas condensate wells because of the high unpredictability of two-phase flow behavior and the changes in gas-liquid ratio with pressure and temperature. There are many analytical and empirical correlations in the literature, that describe the two-phase flow through wellhead chokes in critical conditions. However, the nature of the flow in most gas condensate wells is sub-critical, and using these correlations causes severe errors in the estimation of flow parameters. Also, the models available for sub-critical flow are not providing satisfactory accuracy for prediction and are often difficult to use. The objective of this work is to develop reliable models for predicting choke performance. Artificial intelligent (AI) based models were developed to accurately design the optimum wellhead choke size, based on the required flow rate, pressure difference, and gas to liquid ratio. The newly developed models are accurate and easy to be used, comparing to the existing predictive models. In this work, artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were applied to estimate the gas flow rate from gas condensate wells. Among the developed models, ANFIS with a subtractive clustering approach performed the best with a percentage error of 2.4% and coefficient of determination (R2) of 0.981. The newly developed models can improve production management by proposing the optimum flow parameters that help in regulating the flow rate of two phases from gas condensate wells. Also, they can help in stabilizing the flowing pressure downstream the choke and prevent downhole reservoir formation damage, by providing the required back pressure to avoid excessive drawdown. Overall, the presented models provide an easy
Expanding the Degradable Fluid Loss Additive Application Spectrum Through Advanced Slurry Transport Modeling, Theoretical and Experimental Integration
Authors: Khan Abdul Muqtadir , Isaev Vadim , Plyashkevich Vladimir 
Published: February 2022

AbstractChemistry and the materials portfolio play a central role in fracturing success. Challenges in a achieving successful and productive proppant pack are resolved using varying products and fit-for-purpose chemistry utilization. The conventional understanding of using fluid loss additive (FLA) only for reducing fluid leakoff in high-permeability formations can be extended with advanced digital tools for optimum realization of a broader application spectrum for well-specific challenges.Polylactic acid-based powdered degradable FLA (DFLA) was developed with engineered particle size distribution to plug rock pore throats. Core flow tests were conducted with and without DFLA with borate crosslinked base fluid to measure the performance metrics. The application spectrum was extended beyond the fluid loss control in high-permeability rock to aid in screening out multiple fractures and natural fractures, reducing poroelastic tendency of tight, tectonically influenced formations. An advanced numerical modeling simulation approach was used to evaluate the distribution of the DFLA particles along the fracture cross-section and their dynamics to yield optimum fracture geometry with lesser pad volume.Coreflood tests demonstrated a reduction of up to 40% in fluid loss coefficient and spurt loss components with 30 lbm/1,000 gal DFLA loading. A regained permeability reduction of 12% from the baseline was observed when 25% particulate DFLA mass loss occurred, which can be minimized with higher shut-in times and complete degradation. The spectrum was expanded conceptually for up to eight applications based on literature references. Digitally advanced hydrodynamics and an in situ kinetics simulator were used to accurately model the slurry flow with and without DFLA. The model was extended with a sensitivity study with 32 synthetic cases to extend FLA utilization to medium- and low-efficiency formations. The modeling results showed that more than 50% of crosslinked pad volume could be saved while retaining the same fracture geometry evolution.Industry use of FLA chemistry has been minimal. In the digital age, this is the first and a unique demonstration of how digital tools can aid extending the material portfolio spectrum investigated from laboratory, simulation, and field case perspectives. Multiple applications of FLA can enhance project economics and reduce polymer and fracturing fluid formation damage by lowering the difference between differential pressure at the fracture face and drawdown during cleanup and the production phase.
Novel Stuck Pipe Troubles Prediction Model Using Reinforcement Learning
Authors: Alzahrani Majed , Alotaibi Bader , Aman Beshir 
Published: February 2022

Abstract
Predicting stuck pipe problems during oil and gas drilling operation is one of the most complex problems in the drilling business. The complexity of the problem is driven not only by the complexity of the natural factors, but it extends to the nature of the drilling operation itself. The drilling operation is continuously influenced by a dynamic smart system. The dynamic part of the system is impacted by natural forces like formation related characteristics, and also is impacted by human activities during the operation such as drilling, tripping and hole cleaning. The smartness of this system is driven by the fact that the operation is controlled by a number of experts, i.e. drilling engineers, trying to run the best sequence of operations using best operation parameters to achieve operation objective. At the top of that, the engineers can change their operation plan whenever they find it necessary to address any operational condition, including a potential stuck pipe problem.
In this paper we prove the stuck pipe prediction problem is not a binary classification problem. Instead, we define the stuck pipe prediction problem as a multi-class problem which takes into consideration the dynamic nature of the drilling operation. A reinforcement learning based algorithm is proposed to solve the redefined problem, and its performance and evaluation results is shared in details. The accuracy of the developed algorithm in terms of detecting true stuck pipe events is shown. The results will compare the performance of different machine learning algorithms, which is then used to justify the selection of the best performing method. In addition, we show the accuracy performance improvement through time by employing the feedback channel to retrain the model. The presented method is using a reinforcement logic, in which the solution is connected to the operation reporting to label the solution prediction for false and true predictions. This information is then used to return the neural networks to learn new operational patterns to enhance accuracy.
Application of a Sustainable Cognitive Twin Solution for Drilling Hazard Avoidance and Optimization
Authors: James Chris , Sankaran Sathish , Hinshaw Chris , Fallah AmirHossein , Borjas Ricardo , Adams Craig 
Published: February 2022

Abstract
Model-based methods such as torque and drag, hydraulics, hole cleaning, and downhole dynamics have improved over the past years for pre-job design and post-analysis of drilling operations. However, there is a still a large gap in extending these traditionally manual processes and scaling them up for model-based, real-time decision support systems. Significant opportunities exist in applying these towards drilling hazard avoidance, performance diagnostics, and optimization. It is desirable to have an automated solution that orchestrates data ingestion and model calculations based on physics-based, data driven, or hybrid methods, to analyze real-time field data and enable valuable insights into operations. In this context, cognitive twins refer to the ability to deliver actionable insights for real-time optimization and hazard avoidance through systematic automation of data and model pipelines.
In this work, a sustainable real-time solution is proposed comprising of data aggregation, cleansing, analytics, and cognitive twins to enable the automated orchestration, analysis, and insight delivery from drilling models. This process drives the scalability of the system – ensuring evergreen models and analysis are always available across all rigs and wells in a drilling fleet. Methods of modeling and analysis combine data driven methods with physics-based constraints to compute certain key quantities (e.g., friction factors, pressure deviations, and cuttings bed height). These quantities contain results and intermediate features which can be used for machine learning methods to drive fleet performance optimization above and beyond well-level analysis.
We applied these methods to both onshore and offshore wells in real-time to identify and automatically detect several early warning signs of common drilling problems such as stuck pipe, washouts, and poor hole cleaning. Issues related to friction, hydraulics, cuttings transport, general drilling efficiency, and combinations of the previous categories are translated into actionable insights, which can be immediately implemented in the field to enhance safety, reduce non-productive time, and increase performance. In addition, the insights were also analyzed at a fleet-wide scale to identify patterns and drive operational best practices. Common challenges and solutions relating to implementing such a system at scale are addressed.
This methodology addresses and overcomes many of the challenges involved in applying digital twins in a sustainable, real-time mode. It extends functionality into cognitive twins that can analyze modeled behavior to real-time results and enables automated insights to be generated for single models such as torque and drag in isolation and combinations of models such as the insights derived from interpreting torque and drag, hydraulics, and hole cleaning results simultaneously.
An Integrated Directional Drilling Simulator with Steering Advisor and Self-Learning Algorithm
Authors: Liu Yang , Marck Julien , Tian Kaixiao , Demirer Nazli , Pho Vy 
Published: February 2022

Abstract
The industry has recently put a significant emphasis on optimizing the directional drilling process to reduce non-productive time (NPT) and improve well-bore quality. Mud motors are widely used due to their low operational cost and high reliability. In this paper, a drilling advisory system for mud motors has been developed to provide steering recommendations in real time to maintain smooth wellbore trajectories respective to a given well plan while satisfying operational constraints. The drilling advisory system, which was tested both offline and online on 20 of runs across North America and globally, provides reliable steering decisions relative to the surveys and real-time data, when available. This capability revolutionizes the drilling industry by providing a tool that guides directional drillers toward improving efficiency, consistency, and quality. Moreover, the advisory system serves as the core to the development and implementation of a fully autonomous drilling system.
Not All Unconventional Reservoirs Are Similar: MENA Regional vs. Global Anisotropic Rock Index and Mechanical Characterization – Part 1
Authors: Gramajo Eduardo , Rached Rached 
Published: February 2022

Abstract
Unconventional formations require advanced mechanical and index assessments to improve their understanding under different geomechanical processes. However, difficulties associated with obtaining cores from the target formations and the challenges with sample preparation increase the assessment complexity.
This research compiles data from unconventional index properties and rock mechanical test results from published articles and reports. The parameters include rock mineralogy, rock mechanical properties (compressive strength and elastic properties), and petrophysical properties (porosity, TOC, and permeability). The study showcases the main differences between the global and regional (Middle East and North Africa) rock formations and presents the best analogs for the regional reservoirs. These findings supplement the scarce and complex procurement of the needed rock specimens and reduce the number of core samples required for detailed evaluations. These outcomes help reduce the costs (equipment, sample preparation, measurement time, and the number of specimens tested) associated with the unconventional rock experimental evaluation. In addition, this study explores the successful development strategy implemented in the unconventional reservoirs in China to accomplish commercial production and recommends appropriate rock analogs for detailed experimental evaluations.
This paper is part of an in-depth literature data compilation of MENA regional and global unconventional formations. This section of the study focuses on the target unconventional formations of Saudi Arabia and the unconventional formations in China, the USA, and Canada.
Modeling Frac Hits: Mechanisms for Damage Versus Uplift
Authors: Fowler Garrett , Ratcliff Dave , McClure Mark 
Published: February 2022

Abstract
Parent/child interactions pose a critical challenge for oil and gas shale producers (Roussel et al., 2013; Yaich et al., 2014; Rimedio et al., 2015; Miller et al., 2016; King et al., 2017; Dhuldhoya and Dusterhoft, 2017; Cipolla et al., 2018; Whitfield et al., 2018; Rainbolt and Esco, 2018; Lindsay et al., 2018; Gale et al., 2018; Scherz et al., 2019; Guo et al., 2019; Jin and Zoback, 2019; Kumar et al., 2020; Zheng et al., 2020; Gupta et al., 2020). The industry has progressed significantly in its understanding of causes and mitigation. However, important uncertainties remain. Fracture driven interactions or more commonly, "frac hits", exhibit varied behaviors in different basins. In the majority of basins, parent wells exhibit production loses after a frac hit. We examine and contrast a case study in the Bakken where production uplift occurs to observations of production loss in a STACK case study in Oklahoma. We show the productivity enhancement in the Bakken case study is driven by proppant transport and fracture conductivity amplification, with no apparent skin or conductivity damage. This suggests that absent specific damage mechanisms, frac hits alone can improve productivity via repressurization and increased propped area. In the STACK case study, fracture conductivity damage reactions must be introduced in order to match the historical data. This suggests that in the STACK, and perhaps other basins, additional processes are occurring in the subsurface to hinder the productivity of wells after frac hits. We postulate that minerology, petrophysics, and reservoir condition differences between basins causes differences in impacts of the fracture driven interactions.
Optimization of NMR Permeability Transform and Application to a Source Rock Reservoir
Authors: Chen Jin-Hong , Althaus Stacey , Boudjatit Mohammed 
Published: February 2022

Abstract
Permeability is a fundamentally important property of reservoir rocks that governs the flow of the reservoir fluids and production rates. Many methods have been developed to estimate permeability, including well established and documented laboratory measurements on whole core and plugs, analysis of formation test data, and analysis of production. Obtaining permeability from nuclear magnetic resonance (NMR) T1 or T2 has proven to be a cost effective method that can provide continuous permeability along a wellbore. This method uses a transform function on NMR well log data to calculate permeability. An accurate NMR permeability transform requires calibration to fit local data of a specific field based on measured data from representative cores from the field. The general form of the permeability transform developed for conventional sandstone and carbonate reservoirs does not work well for extremely tight reservoirs such as source rocks. Here we show a generic optimization method to find the optimal permeability transform for any tight reservoirs using NMR log data and laboratory measured permeability data from samples at selected depth of the logged well. This optimization method is applied to a source rock well and a permeability transform was obtained. The transform is a function of the movable fluid in the rock and logarithm mean of the NMR relaxation time. The permeability calculated from the transform is comparable to measured permeability from core samples.
Combining Hydrajetting Perforations and Hydraulic Fracturing Services – Value Added and Lessons Learnt from Operations in CBM Wells in India
Authors: Mirani Ankit , Tripathi Soniya , Srivastav Ashish , Chandak Raj 
Published: February 2022

Abstract
Coal Bed Methane (CBM) development in India has emerged as one of the cleanest solutions to the fuel energy requirements of this energy-starved country. Favorable market scenarios and lucrative gas prices are enabling operators in this business to target aggressive well-completion schedules. This study discusses the techno-economic benefits realized by the operator company of using combined coiled tubing (CT) deployed hydrajetting and fracturing services instead of conventional wireline perforations in CBM wells.
The service company introduced a unique fracturing service that integrates six processes – depth correlation with CT, hydra-jet perforation, hydra-jet fracture initiation, hydraulic fracture stimulation, zonal isolation using a sand plug, and wellbore cleanout using CT. It completes these processes in one single trip-in-hole, making the service cost and time efficient and eliminating the use of wireline for perforating and setting bridge plugs in the well which requires multi-stage fracturing. The technology in use, lessons learned, and knowledge gained from operations in India are shared in this paper.
The process employs a customized CT bottom-hole assembly (BHA) at the core of its service. Customized engineering solutions for hydrajetting can be developed based on casing specifications, cementing conditions, and stimulation design. The principle of hydrajetting perforations and the BHA details are discussed along with its benefits over the alternative techniques. The experience gained during operations allowed the service company to optimize jetting flow rates, differential pressures, and back pressures to improve its operational efficiency and also allow maximum proppant to be placed into the formation being stimulated. Implementing the lessons learned increased the hydrajetting tool life from 25-30 sets of perforation to about 40 per tool. The paper also discusses job design improvements implemented to prevent sanding up the wellbore leading to stuck CT. Finally, the paper discusses the economic benefits achieved by the operator company leading to increased productive time and a faster rate of well completion.
CBM fields require excessive dewatering before they break out gas and become commercially viable projects. The technology discussed in this paper enables the operators to put the maximum number of wells on production, in a shorter period maximizing the Net Present Value (NPV) of the asset.
GHG Emission Reduction at First Saudi Aramco CC&I Project
Authors: Ahmari Sultan , Mufti Abdullatef 
Published: February 2022

Abstract
The paper objective is to demonstrate the successful operation & maintainability of Carbon Capturing & Injection (CC&I) Project. CC&I project is one of the major Saudi Aramco projects to reduce Greahhouse Gaseous (GHG) emissions as one of the company key environmental aspects. The project is located in the Eastern Province of the Kingdom of Saudi Arabia at Hawyiah NGL Recovery Plant (HNGLP).
The project started capturing the CO2 in 2015 from HNGLP Acid Gas Removal Units (AGRUs) and then inject nearly 750 Kton per year of carbon dioxide into oil wells for sequestration and enhanced oil recovery maintainability. In most of gas plants, acid gas (primarily CO2 and H2S) is treated till achieving certain air quality parameters before thermal oxidizing process. After CC&I project, acid gas (only CO2 for HNGLP) is captured inlieu of thermal oxidizing. CC&I project has several processes from CO2 capturing to injection as follows: integrally geared multistage compressor, standalone dehydration system using Tri-Ethylene Glycol (TEG), CO2 recovery system, Granulated Activated Carbon (GAC) to treat water generated from compression and dehydration systems for reuse purpose, and special dense phase pump that transfers dehydrated CO2 at supercritical phase through 85 km pipeline to one of South Ghawar Field Wells to replace typical oil recovery enhancement technique (sea water injection). CC&I project is unique as it has several new technologies and experiences represented by the compressor capacity, pumping supercritical fluid at very high pressures, using mechanical ejector application to maximize carbon recovery, and using TEG as dehydration medium with CO2. CC&I project design considered the noise hazards generated from the compressor operation via installing engineering enclosure with proper ventilation system.(1)
This project helped HNGLP to minimize the total GHG emissions from combustion sources by nearly 30-35% comparing with before. It also helped to reduce nearly 75-80% of the consumed fuel gas to run the thermal oxidizers in AGRUs and send it as product as sales gas. The project helped to increase HNGLP business sustainability performance measures (less energy consumption by 8-9% and nearly 5-6% water reserves sustainability).