bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Leap into Automation for Advanced Fracture Characterization
Authors: Patro Radhika , Mishra Manas , Chawla Hemlata , Devkar Sambhaji , Sinha Mrinal , Mukherjee Nistha 
Published: December 2021

Abstract
Fractures are the prime conduits of flow for hydrocarbons in reservoir rocks. Identification and characterization of the fracture network yields valuable information for accurate reservoir evaluation. This study aims to portray the benefits and limitations for various existing fracture characterization methods and define strategic workflows for automated fracture characterization targeting both conventional and unconventional reservoirs separately.
While traditional seismic provides qualitative information of fractures and faults on a macro scale, acoustics and other petrophysical logs provide a more comprehensive picture on a meso and micro level. High resolution image logs, with shallow depth of investigation are considered the industry standard for analysis of fractures. However, it is imperative to understand the framework of fracture in both near and far field. Various reservoir-specific collaborative workflows have been elucidated for a consistent evaluation of fracture network, results of which are further segregated using class-based machine learning techniques.
This study embarks on understanding the critical requirements for fracture characterization in different lithological settings. Conventional reservoirs have good intrinsic porosity and permeability, yet presence of fractures further enhances the flow capacity. In clastic reservoirs, fractures provide an additional permeability assist to an already producible reservoir. In carbonate reservoirs, overall reservoir and production quality exclusively depends on presence of extensive fracture network as it quantitatively controls the fluid flow interactions among otherwise isolated vugs.
Devoid of intrinsic porosity and permeability, the presence of open-extensive fractures is even more critical in unconventional reservoirs such as basement, shale-gas/oil and coal-bed methane, since it demarcates the reservoir zone and defines the economic viability for hydrocarbon exploration in reservoirs.
Different forward modeling approaches using the best of conventional logs, borehole images, acoustic data (anisotropy analysis, borehole reflection survey and stoneley waveforms) and magnetic resonance logs have been presented to provide reservoir-specific fracture characterization. Linking the resolution and depth of investigation of different available techniques is vital for the determination of openness and extent of the fractures into the formation.
The key innovative aspect of this project is the emphasis on an end-to-end suitable quantitative analysis of flow contributing fractures in different conventional and unconventional reservoirs. Successful establishment of this approach capturing critical information will be the stepping-stone for developing machine learning techniques for field level assessment.
Development and Implementation of Two Compression Set Tools in a Wellbore Clean Out String
Authors: Maher Peter , Nelson Carl , Dockweiler David 
Published: December 2021

Abstract
Running two compression set tools in a single wellbore clean out string, typically a bypass tool and negative test packer, has been a significant industry challenge to operate reliably. The need for running these types of tools is generally driven by the need to perform a negative test on a liner top and achieve high flow rates necessary to hydraulically remove debris from the well. Combining these operations into a single run is an increasingly common method to reduce rig time and cost for the operator. Tools to perform this type of operation are generally available from many service providers, however difficulties and challenges arise when trying to manipulate two different tools in the same string that function by the same compression set method. These operations do have a history that is partially successful, however on a long term basis reliability is generally considered poor by most operators, as a failure to manipulate the tools correctly can result in a failed run and a trip out of the hole. This paper discusses the development and successful field deployment of a system of two compression set tools to address this specific challenge while improving reliability over existing solutions.
Geomechanical Properties Estimation Utilizing Artificial Intelligence Prediction Tool
Authors: Alabbad Mohammed , Alqam Mohammad , Aljeshi Hussain 
Published: December 2021

Abstract
Drilling and fracturing are considered to be one of the major costs in the oil and gas industry. Cost may reach tens of millions of dollars and improper design may lead to significant loss of money and time. Reliable fracturing and drilling designs are governed with decent and representative rock mechanical properties. Such properties are measured mainly by analyzing multiple previously cored wells in the same formation. The nature of the conducted tests on the collected plugs are destructive and samples cannot be restored after performing the rock mechanical testing. This may disable further evaluation on the same plugs.
This study aims to build an artificial neural network (ANN) model that is capable of predicting the main rock mechanical properties, such as Poisson's ratio and compressive strength from already available lab and field measurements. The log data will be combined together with preliminary lab rock properties to build a smart model capable of predicting advance rock mechanical properties. Hence, the model will provide initial rock mechanical properties that are estimated almost immediately and without undergoing costly and timely rock mechanical laboratory tests. The study will also give an advantage to performing preliminary estimates of such parameters without the need for destructive mechanical core testing.
The ultimate goal is to draw a full field geomechanical mapping with this tool rather than having localized scattered data. The AI tool will be trained utilizing representative sets of rock mechanical data with multiple feed-forward backpropagation learning techniques. The study will help in localizing future well location and optimizing multi-stage fracturing designs. These produced data are needed for upstream applications such as wellbore stability, sanding tendency, hydraulic fracturing, and horizontal/multi-lateral drilling.
Combined Proppant Placement and Early Production Modeling Achieve Increased Fracture Performance in Ecuador's Oriente Basin
Authors: Chertov Maxim , Salazar Suarez Franck Ivan , Kaznacheev Mikhail , Belyakova Ludmila 
Published: December 2021

Abstract
In the paper, we document one iteration of the continuous improvement of well performance undertaken in the Oriente Basin in Ecuador. In the past, it had been observed that well economics was sometimes degraded by the issues related to proppant flowback from hydraulic fractures. Proppant flowback resulted in extra costs from well cleanouts, pump replacement, and damage to fracture conductivity. After evaluation of proppant flowback cases using the combined modeling workflow that simulates fracture growth, proppant placement, and early production of solids and fluids, it had been proposed to modify fracture designs and well startup strategy. In this paper, we review the first results of implementation of these modifications in the field and evaluate the significance of improvements.
Applying Boundary Element Simulation to Generate Stress-Based Fracture Drivers: A Case Study for the Montney Unconventional Oil/Gas Play in Western Canada Basin
Authors: Cai Zhong , Smith Craig , Cole John , Tan Chee Phuat 
Published: December 2021

Abstract
Natural fracture distribution is critical to the hydrocarbon production from the Early Triassic Montney unconventional oil and gas play. The formation underwent several tectonic events, creating a unique natural fracture system. Identifying tectonic events and their stress field evolution is an import component in fracture system modeling and prediction. The objective of this paper is to identify the evolution of paleo-stress domains, to establish related tectonic models, and to generate the drivers for fracture network modeling which will aid in reservoir understanding and overall play development.
Compared with other geomechanical approaches, the boundary element method (BEM) is better suited for the structural characteristics in the study area. Hence, the corresponding boundary element simulation (BES) was applied for the evolution of the paleo-stress domains. The methodology is a combination of 3D BEM and Monte Carlo simulations. The inputs include seismic interpreted faults and natural fractures from Formation Microimager logs. After applying the methodology, several best fit realizations were calculated, and the admissible paleo-stress domains were characterized by the tectonic models which are consistent with the regional tectonic evolution of the formation.
The study area is about 400 km2 located at northeast British Columbia in the Western Canada Basin. The main structural features are the thrust and back-thrust faults, forming different fault blocks without any significant deformation structures. The Montney formation within the study area underwent several tectonic events: (1) regime of terrane collision, indentation and lateral escape during end of Middle Jurassic to Middle Cretaceous; (2) regime of left-lateral transpression dominated by strike-slip during end of Late Cretaceous and Paleocene; and (3) regime of right-lateral transtension dominated by strike-slip during end of Early and Middle Eocene which is maintained till present day. Three major stress domains were identified in the study area by the application of the BES method, one reverse event and two strike-slip events, representing paleo and present-day stress domains. These stress domains are consistent with the regional tectonic evolution history of the foreland basin. The stress field parameters, such as stress ratio and maximum horizontal stress azimuth, are consistent. The derived tectonic models are shown to be reliable drivers for the subsequent fracture modeling and geomechanics study.
First Oman Implementation of Pressurized Mud-Cap Drilling Improves Drilling Efficiency and Sustainability
Authors: Orta Cesar , Al Faqih Mohanad , Al Gharibi Bader , Al Shabibi Mohammed , El Khouly Ali , Matin Yasir , Hadj-Moussa Ayoub , Saleh Mujahed 
Published: December 2021

Abstract
Drilling with a gas cap over the Natih formation in Oman often results in excessive flat time. Using the current dynamic fill equipment to deal with kick and loss scenarios leads to extensive nonproductive time on the rig. Managed pressure drilling (MPD) is a well-established drilling technology, and diverse variants exist to suit different requirements. All those variants use the rotating control device (RCD) as a common piece of equipment, but their procedures are different. The pressurized mud-cap drilling (PMCD) technique in the Natih formation replaces the need for traditional dynamic filling technology. The PMCD application enhances the drilling and completion processes by reducing flat time when total downhole losses are experienced.
This paper elaborates on PMCD as a proven drilling technique in total loss scenarios when drilling with it for the first time in the Natih formation in Oman. It describes the PMCD process, the associated equipment, and the results of the inaugural application in the Qalah field.
Predict Geomechanical Parameters with Machine Learning Combining Drilling Data and Gamma Ray
Authors: Martinelli Mattia , Colombo Ivo , Russo Eliana Rosa 
Published: December 2021

Abstract
The aim of this work is the development of a fast and reliable method for geomechanical parameters evaluation while drilling using surface logging data. Geomechanical parameters are usually evaluated from cores or sonic logs, which are typically expensive and sometimes difficult to obtain. A novel approach is here proposed, where machine learning algorithms are used to calculate the Young's Modulus from drilling parameters and the gamma ray log. The proposed method combines typical mud logging drilling data (ROP, RPM, Torque, Flow measurements, WOB and SPP), XRF data and well log data (Sonic logs, Bulk Density, Gamma Ray) with several machine learning techniques. The models were trained and tested on data coming from three wells drilled in the same basin, in the same geological units but in different reservoirs. Sonic logs and bulk density are used to evaluate the geomechanical parameters (e.g. Young's Modulus) and to train the model. The training phase and the hyperparameter tuning were performed using data coming from a single well. The model was then tested against previously unseen data coming from the other two wells.
The trained model is able to predict the Young's modulus in the test wells with a root mean squared error around 12 GPa. The example here provided demonstrates that a model trained with drilling parameters and gamma ray coming from one well is able to predict the Young Modulus of different wells in the same basin. These outcomes highlight the potentiality of this procedure and point out several implications for the reservoir characterization. Indeed, once the model has been trained, it is possible to predict the Young's Modulus in different wells of the same basin using only surface logging data.
Artificial Intelligence AI Assisted Thermography to Detect Corrosion Under Insulation CUI
Authors: Amer Ayman , Alshehri Ali , Saiari Hamad , Meshaikhis Ali , Alshamrany Abdulaziz 
Published: December 2021

Abstract
Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets where the oil and gas industry is not immune. Its severity arises due to its hidden nature as it can often times go unnoticed. CUI is stimulated, in principle, by moisture ingress through the insulation layers to the surface of the pipeline. This Artificial Intelligence (AI)-powered detection technology stemmed from an urgent need to detect the presence of these corrosion types.
The new approach is based on a Cyber Physical (CP) system that maximizes the potential of thermographic imaging by using a Machine Learning application of Artificial Intelligence. In this work, we describe how common image processing techniques from infra-red images of assets can be enhanced using a machine learning approach allowing the detection of locations highly vulnerable to corrosion through pinpointing locations of CUI anomalies and areas of concern. The machine learning is examining the progression of thermal images, captured over time, corrosion and factors that cause this degradation are predicted by extracting thermal anomaly features and correlating them with corrosion and irregularities in the structural integrity of assets verified visually during the initial learning phase of the ML algorithm. The ML classifier has shown outstanding results in predicting CUI anomalies with a predictive accuracy in the range of 85 – 90% projected from 185 real field assets. Also, IR imaging by itself is subjective and operator dependent, however with this cyber physical transfer learning approach, such dependency has been eliminated. The results and conclusions of this work on real field assets in operation demonstrate the feasibility of this technique to predict and detect thermal anomalies directly correlated to CUI.
This innovative work has led to the development of a cyber-physical that meets the demands of inspection units across the oil and gas industry, providing a real-time system and online assessment tool to monitor the presence of CUI enhancing the output from thermography technologies, using Artificial Intelligence (AI) and machine learning technology. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the associated scaffolding and downtime.
Capturing the Dynamics of Foam Coarsening in a HPHT Microfluidic System
Authors: Yu Wei , Zhou Xianmin , Kanj Mazen Yousef 
Published: December 2021

Abstract
The foam coarsening process is significant to foam stability in porous media. This study provides, for the first time, direct visualization of the foam coarsening process in porous media under realistic reservoir conditions. Foam coarsening behavior in porous media has shown a similar linear increase in the average bubble area to that in an open system but differs in two stages. The average bubble area increases with a faster rate in stage 1 and then increases with a slower rate in stage 2 and stage 2 dominates the foam coarsening process. The transition between the two stages occurs as the inner bubbles disappear when the edge bubbles start feeling the effects of the walls. The foam at steady-state shows a bimodal size distribution with bubbles trapped in the pore bodies and pore throats. The effects of pore pressure (600-3200 psi) and temperature (22-100 °C) were studied. Foam coarsening dynamics are sensitive to pore pressure and temperature, where higher pore pressure and lower temperature are more favorable to maintain a stable foam. Finally, the coarsening rates of foams generated with different gas phases were compared. In contrast to N2 foam and gas CO2 foam, supercritical CO2 foam exhibits the slowest coarsening rate because of its ultralow interfacial tension.
SmartWater Synergy with Microsphere Injection for Permeable Carbonates
Authors: Cao Dongqing , Han Ming , Saleh Salah , Ayirala Subhash , Al-Yousef Ali 
Published: December 2021

Abstract
This paper presents a laboratory study on combination of SmartWater with microsphere injection to improve oil production in carbonates, which increases the sweep efficiency and oil displacement efficiency. In this study, the properties of a micro-sized polymeric microsphere were investigated including size distribution, rheology, and zeta potential in SmartWater, compared with conventional high salinity injection water. Coreflooding tests using natural permeable carbonate cores were performed to evaluate flow performance and oil production potential at 95°C and 3,100 psi pore pressure. The flow performance was evaluated by the injection of 1 pore volume microspheres, followed by excessive water injection. Oil displacement tests were also performed by injecting 1 pore volume of microspheres dissolved in SmartWater after conventional waterflooding.
The median particle size of the microsphere in conventional injection water with a salinity of 57,670 ppm was about 0.25 µm. The particle size was increased by 50% to 100% with reduced elastic modulus when the microsphere dispersed in SmartWater with lower salinity. The zeta potential value of microsphere was decreased in SmartWater compared to that in conventional injection water, showing more negatively charge property. Flow performance of microsphere solutions in the carbonate cores was found to be dependent on their particle size, strength, and suspension stability. The results from coreflooding tests showed that the microsphere dispersed in SmartWater would result in higher differential pressure than that observed in conventional injection water. The SmartWater caused the microspheres swell to larger but softer particles with better suspension stability, which enhanced both the migration and blocking efficiency of microsphere injection. The oil displacement tests confirmed that the microsphere in SmartWater displaced more oil than that obtained with conventional injection water. This result was clearly supported by the higher differential pressure from microsphere injection in SmartWater. The oil bank appeared historically in the post water injection stage, which was quite different from the reported findings of typical mobility controlling agents in the existing knowledge. The microspheres were observed in the core flood produced fluids, indicating the improvement of microsphere migration by SmartWater. This work, for the first time, demonstrated that the combination of SmartWater and microsphere injection yields additional oil production. The proposed hybrid technique can provide a cost-effective way to improve waterflooding performance in heterogeneous carbonates.
Reservoir Engineering and Geomechanical Aspects of Well Plugging and Abandonment
Authors: Qi Qianru , Ghaliah Khoja , Ershaghi Iraj 
Published: December 2021

Abstract
With the maturation of many oilfields, further well abandonments will occur in the years to come. There are issues about improper well abandonment that can have far-reaching effects for responsible companies or entities. At this time in the US, where most of the operation is operated by non-government entities, sometimes the sovereign state may end up covering the cost of well abandonment when the operator is not financially capable in managing such costs. That will be a burden to the public taxpayers. In this paper, we review an important aspect of the well abandonment practices and at present, based on a reservoir modeling approach, more clearance on the potential formation of free gas that can be a cause of concern. We also discuss the integrity issues of the sealing process. We point out how the development of cracks caused by many factors, including geomechanical effects or slow deterioration of the cement seal, in the long run, may result in generating escape paths for the evolved hydrocarbon gases.