bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding Lateral Permeability Variations Using Integrated Logging-While-Drilling Azimuthally Oriented Formation Testing and High-Resolution Imaging in Heterogeneous Carbonate Reservoirs
Authors: Kumar Ashok , Fateh Ahmed , Taher Ahmed , Fouda Mohamed 
Published: March 2023

Abstract
Permeability is a fundamental petrophysical attribute required to accurately evaluate recoverable reserves and design an appropriate field-development strategy. Because logging tools do not measure absolute permeability, minimizing uncertainty in the evaluation of log-derived permeabilities remains one of the most critical petrophysical challenges in the oil industry. Horizontal development in laterally heterogeneous carbonate reservoirs also requires evaluation of lateral permeability variations to optimize completion design, while maximizing reservoir exposure via precise well placement in real time. This paper demonstrates innovative methods to evaluate lateral permeability variations in heterogenous carbonate reservoirs.
The workflow for log-derived permeability predictions is based on empirical relationships using nuclear magnetic resonance (NMR), acoustic, and high-resolution imaging tool measurements. These are normalized in an integrated multi-disciplinary approach using core, well test, production logs, and formation-tester mobility data where available. Traditionally, formation-tester tools have been used to obtain single pressure and mobility values at each test station. The logging-while-drilling (LWD) formation tester can be oriented azimuthally to help evaluate permeability anisotropy, which is a key factor for reservoir characterization in laterally heterogeneous reservoir layers. The oriented data can also be used to adjust the well plan in real time to maximize reservoir exposure in the desired "sweet spot."
Variations in the oriented LWD formation tester measurements at each depth station exhibited favorable correlations to azimuthal changes observed in the LWD high-resolution micro resistivity image. Detailed image analysis further helped to understand the mechanism that governs the azimuthal permeability profile. The combination of oriented LWD formation-tester and high-resolution image data also aided in making better real-time geosteering decisions, as well as in the planning and design of a future field-development program within the local reservoir sector. Operational considerations to maximize data quality rely on an optimized bottomhole assembly (BHA) design, accurate depth control, and robust orientation techniques based on best practices and lessons learned.
This paper presents an integrated approach for well placement and an improved understanding of flow-unit characterization via first-time use of oriented formation-tester data in conjunction with corresponding high-resolution images in a laterally heterogeneous reservoir.
Shale and Carbonate Wellbore Stability Charts for Drilling Operations
Authors: Al Ajmi Ahmed , Shaheed Bilal , Al Ajmi Adel , Al-Hadrami Hamoud , Al-Aamri Mohammed 
Published: March 2023

Abstract
The oil industry suffered a great deal of time and financial damage because of wellbore instability problems reflected by well collapsing. This suggests that more meticulous well drilling planning are required to avoid such rock failure. Shale has been a difficulty for many drilling operators, and shale instability difficulties are to blame for 90% of borehole collapse problems. Additionally, compared to shale or sandstone, carbonate rocks are believed to be more capable and to face less instability problems. However, carbonate-based reservoirs make up the majority of the world's hydrocarbon reserves. As a result, a sizable number of wells are drilled in carbonate deposits every year. Due to this, there are now more wellbore instability problems being experienced while drilling carbonate formations globally.
Rock failure will occur if the field's rock mass is subjected to a huge stress beyond what it can withstand. Construction of models that can foresee the conditions in which a rock can fail will greatly aid in preventing serious problems like borehole collapse and solids generation. Therefore, by accurate geomechanical modeling, the most suitable mud density and borehole trajectory should be understood in advance before drilling. The Mogi-Coulomb failure criterion and a linear elastic stress model for the field are utilized in this study as a geomechanical stability model. The effects of all the major stresses in the fields are reflected and depicted in this three-dimensional stability model. For the benefit of all drilling engineers and operators in Oman, various charts are produced using the stability model.
Wellbore stability study is generally a difficult task that may need for a highly specialist consultation and is a time-consuming procedure. An important requirement that is overlooked in the oil and gas sector is the need to simplify the wellbore stability analysis in a way that makes it simple and ready for direct use by drilling engineers. The stability charts give the minimum allowable mud density for shale and carbonate rocks to prevent borehole collapse for all drill trajectories. Due to the emphasis on Oman's shale and carbonate formations and the most common possibilities, the created charts are limited for ranges of rock geomechanical characteristics. According to the input parameters, the produced stability charts are divided into several groups. The effect of each geomechanical property is studied to understand the relationships among them. The produced charts for shale and carbonate deposits in Oman and elsewhere are used in actual field case studies. Some of these field applications have already been carried out, while others are still in the works and will likely be discussed in a future project.
Identifying and Ranking Multiple Source Models for Transfer Learning in Unconventional Reservoirs.
Authors: Cornelio Jodel , Mohd Razak Syamil , Cho Young , Liu Hui-Hai , Vaidya Ravimadhav , Jafarpour Behnam 
Published: March 2023

Abstract
When a limited number of wells are drilled at the early stages of developing unconventional fields, the available data is insufficient for developing data-driven models. To compensate for the lack of data in new fields, transfer learning may be adopted by using a previously learned model/knowledge from similar fields (source data) to build a predictive model for the new field. To be effective, transfer learning requires the source and target fields to have similarities and to ensure relevant information/knowledge is transferred. The transfer of irrelevant knowledge may impede the training process and lead to a negative knowledge transfer. When multiple source data are available, it is important to identify each source data's relevance and potential contribution to the target data. We introduce a framework to rank different source datasets and determine their capability for transfer learning. The methodology relies on using knowledge learned from datasets with similar features to the target dataset. This methodology helps circumvent the data needs for training while ascertaining that the right knowledge is transferred when developing new fields. Additionally, the framework allows for combining relevant features from multiple source models (with similar ranks). It allows for transferring the knowledge learned from mature fields to improve the performance of deep learning proxy models for new fields with similar features.
Sustainable Energy for Rotating Equipment: Train Driver Technology Selection and Lifecycle Impact Assessment
Authors: Abu Sanah Hassan , Goretzki Peter 
Published: March 2023

Abstract
In line with sustainable energy utilization and global decarbonization effort to reach net-zero emission targets, a closer look to variable speed Electrical Drives (ED) as system for Rotating Equipment (RE) is investigated to assess the impact of drive selection towards achieving higher operational excellence in conjunction with end-user and plant operator operation excellence targets. The paper briefly introduces main available variable speed ED systems with relevant attributes for holistic Total Cost of Ownership (TCO) assessment approach that can be streamlined for any RE application in energy industries with pre- defined application-specific boundary conditions.
Paper focuses on RE asset such as centrifugal gas compressors and pumps driven by means of Electric Motor (EM) in oil and gas, petrochemicals, and energy industry. Holistic TCO approach covers practical methodology of impact calculation for ED systems. Namely Mechanical Variable Speed Drive (MVSD), Electro-Mechanical Drive (EMD), or Variable Frequency Drive (VFD) system technologies. Established valuable insight from system-wise TCO assessment provides decision makers with reliable feasibility and data-driven conclusion measure to select most sustainable system for RE operation with variable speed over desired lifetime cycle as TCO holistic approach covers ED systems CAPEX and OPEX respectively.
System TCO approach is further presented as deployed on real project assessment in Middle East for a compressor drive running at 10,900 kW rated power and 10,000 rpm. With Holistic TCO assessment including commercial and ecosystem requirements, EMD and MVSD solutions respectively demonstrated higher success measures as sustainable ED solutions over compressor train lifecycle. Benefits for each ED technology is briefly discussed with its relevant carbon footprint saving potential through equivalent CO2 emissions from consumed energy. Finally, brief highlight on recent successful project deployment of MVSD technology is presented for compressor drive in bio-fuel application as adaptability potential to current market agile shift in sustainable energy sources and various application operability requirements demand.
Optimizing the Hydraulic Fracturing Fluid Systems Using the Completion and Production Data in Bakken Shale
Authors: Alotaibi Najd , Dursun Serkan 
Published: March 2023

Abstract
Objectives/Scope. Well completion is an important step for every well to undergo in order to prepare it for oil and gas extraction. Based on the nature and characteristics of an oil and gas reservoir, appropriate well completion practices are selected to enhance the production. Hydraulic fracturing is one such technique. It frequently involves horizontal drilling and injecting fluids under high pressure to fracture the rock. The larger fractures along with the injected fluid enable high amounts of trapped natural gas and crude oil to flow out of the formation to the producing well bore. In well completion, a variety of chemicals are employed to leverage oil production, and the goal of this study is to determine how such chemicals impact performance rate in several unconventional wells in the Bakken Shale.
Methods, Procedures, Process. In this approach, two Completion and Production datasets from North Dakota (the Bakken Shale) and fracFocus were processed and combined accordingly which resulted in some of the following parameters, type of chemical and amount of chemical, and true vertical depth of the wells. And the dataset that was produced was analyzed based on the stimulation treatment. The proposed workflow utilizes supervised machine learning algorithms to train different predictive models to estimate the amount of the produced oil; including but not limited to neural Random Forest, CATboost and XGboost. Additionally, by quantifying each chemicals’ importance on oil production, this investigation was able to determine each chemical's influence.
Results, Observations, Conclusions. This study examined the impact of more than 2500 different completion chemicals on the oil production of unconventional reservoir and discovered the chemicals with the highest significance on the oil production, given that, the predictive models were able to estimate the oil production accurately after feeding it with the type and measures of the most influencing chemicals.
Novel/Additive Information. The most important pillar of this framework is that it expedites the workflow of hydraulic fracturing jobs in the unconventional reservoir by providing an accurate model that optimizes its parameters to maximize the oil production rate. This solution offers an automated decision-making process for the selection of chemical types to be used in the hydraulic fracking jobs. The choice of chemicals in fracturing fluids is affected by many variables, including its compatibility with the target rock formation to be hydraulically fractured, the geology of the rock formations being drilled through, the pressure and temperature measurements in the target formation, cost, operator preference, and possible interactions between chemicals in the treatment fluid.
Well Design Re-Engineering: First Level of Emission Footprint Optimization
Authors: Nifantov Aleksandr , Ruzhnikov Alexey 
Published: March 2023

Abstract
Well construction is one of the greenhouse gas (GHG) emissions contributors in the oil and gas industry. Today, the main attention is paid to technology solutions, minimizing drilling time and rig upgrading with low-energy equipment. However, what about optimum well design itself, which may lead to acceptable operational risks, reasonable budget, lower power demand and consequently lower emissions to deliver it? The work presented in this paper is about the planning process to reach not just a technical and financial balance in the construction of a well, but also incorporate sustainability elements into the design process itself.
The conventional engineering approach to designing a well, namely being able to meet its technical targets within reasonable budget, was upgraded with an additional workflow to consider the GHG emission footprint of well construction process. Specifically, it was incorporated in a digital solution which enables a comparison between different well program scenarios, from which an optimal design can be chosen. The process includes the following step: 1) creation of a well design meeting its technical targets; 2) estimation of the well budget; 3) modeling the well construction GHG emissions for the different designs. Some iterations of this process are needed to reach sustainable well design (relatively optimal case).
The real extended reach drilling (ERD) well proposal was used as the example for this study. The 3D geomechanical model was created prior to start the well engineering. It simulated distribution of stresses which enabled calculation of the safe pressure window while drilling, tripping, running casing and cementing. Considering this model, the originally proposed casing design was upscaled to achieve the ECD (Equivalent Circulating Density) in the required range without collapsing and breaking down the wellbore wall. Additionally, the trajectory was optimized to improve hole cleaning and facilitate the running of casing in highly deviated sections of the well. All these measures theoretically minimized the risk of not hitting the planned targets. Then, the well profile was tuned to improve the financial impact and operational emission footprint without compromising technical solution. As a result, it had led to potential lowering of the well construction budget by ~2 % and reduction of the emission by 6.3 % (for the sum of Scope 1 and 3 emissions).
The main idea of this approach is to achieve a low-energy, cost-effective and technically feasible well design as a baseline. Within the constraints of this design, it will then leverage specific technology solutions to further minimize energy consumption and GHG emissions.
This manuscript presents a new methodology for the engineering design process for oil and gas wells, which quantitively incorporates sustainability. The ideas, which are expressed in the manuscript, can be implemented in any drilling project worldwide.
Smart Algorithms for Determination of Interfacial Tension (IFT) between Injected Gas and Crude Oil – Applicable to EOR Projects
Authors: Kalam Shams , Khan Mohammad Rasheed , Shakeel Muzammil , Mahmoud Mohamed , A. Abu-khamsin Sidqi 
Published: March 2023

Abstract
Rampant move towards the energy transition landscape has propelled the need to reduce and manage greenhouse gas (GHG) emissions. To this end, it is pivotal to explore cost-effective avenues to utilize GHG and flue gases that can lead to achieving a reduction in emissions target and provide EOR opportunities. Accordingly, the injection of these gases in oil reservoirs allows for hitting two targets with one arrow. Nevertheless, the success of these projects is dependent on accurate IFT estimates between injected gas and resident hydrocarbons. Therefore, this paper evaluates the utility of machine learning algorithms for the estimation of IFT between a mixture of CO2:N2 and straight-chain alkanes for variable temperature/pressure conditions.
A systematic approach is adopted to implement predictive models for IFT prediction by utilizing over 250 experimental data points from the available literature. A comprehensive statistical analysis is performed to achieve model generalization capabilities and improve control over the most relevant input parameters. Consequently, IFT is demarcated as a function of four readily available inputs with pressure and temperature being the most obvious, while carbon number and flue gas mole fraction is also incorporated as critical parameters. Various smart approaches in this work are proposed through the development of an IFT predictor using Artificial Neural Network (ANN), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) algorithms. Machine learning model training is enhanced using a K-fold cross-validation technique combined with the exhaustive grid search algorithm. Post-training, the developed models are tested for reliability using blind datasets reserved for this purpose.
A fair comparison between model efficiency is ensured by using an in-depth error analysis schema that includes various metrics like the correlation of determination, average error analysis, graphical error analysis, and scatter plots. This allows a relative ranking system to be generated that weighs various factors to classify one model as the most efficient. For the IFT prediction problem, it was found that the neural network was aptly able to yield high efficiency and low errors. This stems from the way ANNs function to map the non-linearity relationship between carbon number and the IFT. It was also observed that enhancement of the intelligent model training through multiple techniques resulted in optimized hyperparameters/parameters.
Iterative Integrated Planning and Scheduling Model in Project Management
Authors: Bopalia Vinod Savji 
Published: March 2023

Abstract
Quite often, project planning and scheduling are used interchangeably but they have different roles in the successful completion of a project. On a higher level, the project plan is the master blueprint which drives the project towards achieving objectives while the project schedule details the specific tasks to be executed by individual or set of groups to achieve project's goal.
Project planning primarily involves selecting the appropriate procedures and methodologies required to deliver the project on time. Project scheduling, on the other hand, converts the defined plans, scope, and cost into an operational timeline with specific set of objectives within allocated budget. Main factors in planning and scheduling those differentiate these two phases in the project management specified in the below table. FactorsPlanningSchedulingDeals withWhat and HowWhen and WhoDefinitionPlanning is process of developing a tool to design and produce the desired unique deliverables.Scheduling is the process that defined the time frame in which the project's deliverables will be accomplished and the sequence of tasks.Focus onOverall project objectives and business value creation.Assign various sets of tasks to the available resources for a specified time duration.ImpactFrom beginning until end of the project.During project execution and closure.
Some organizations integrate the jobs and functions of planning and scheduling in a single position. There are benefits to this approach because a project scheduler can't fully understand the nitty gritty when it's time to have resources on the floor without also having firsthand information about the project's entire scope in place. This approach gives remarkable results when the project is small to medium with less engineering disciplines involved. However, for the medium to large scape project will require dedicated functions of planning and scheduling independently.
3D Printed Rocks - An Emerging Technology for Systematic Petrophysical Studies
Authors: Ma Shouxiang , Jin Guodong , Antle Ryan , Wieneke Brian 
Published: March 2023

Abstract
3D printing translates digital models into physical objects, which could potentially be used to print proxies of reservoir rocks from their high-resolution images acquired by such as micro-CT tomography. This paper reviews current 3D printing technologies and explores the capabilities of 3D stereolithography (SLA) technique in rock printing in terms of scale, resolution, accuracy, and repeatability, with the eventual objective of studying factors affecting petrophysical models, such as Archie model, by varying petrophysical inputs of Archie parameters m and n, systematically.
A 3D printer with the resolution of 10 μm is used to print rock models. Two types of digital models are designed for the 3D printing: Model I contains straight cylindrical pores; 19 pores with diameters from 10 to 100 μm with an increment of 5 μm, and Model II is a virtual core of 1 inch diameter and 2 inch length, created from a computer-generated random uniform sphere pack with a porosity of 30%.
Model I cylindrical pores of down to 10 μm are printed and clearly observed on their micro-CT images. Pore connectivity is well preserved in the print proxy. However, the printed pore shapes are not completely circular as designed, indicating a challenge of shape preserving in printing. Pore sizes vary along the axis with a standard deviation of approximately 2-3 μm. In Model II virtual core printing, high printing accuracy and repeatability are achieved, while issues of converting from the digital design model to printer recognized STL model are discovered and being resolved.
With continuous advancements in high resolution imaging, digitalization, and computing power, 3D printing could become a unique and innovative approach enabling manufacturing multiple rock samples for repeatable experiments with identical samples, experiments with systematic variables of such as pore structure or wettability. Challenges faced for printing full-scale pore-structure driven samples can leverage future development and applications of the evolving 3D printing technology.