bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comprehensive Study of Offshore Gulf of Mexico Shale when Exposed to Several Reservoir and Completion Fluids
Authors: Pietrangeli Gianna , Alvarez Daniel , Ceballos Alice , Quissak Felipe , Hayden Laurie 
Published: February 2022

Abstract
Ensuring shale stability while drilling the reservoir and completing a well is critical to guarantee well integrity and to enable the reservoir to product at its maximum potential.
Drill-in fluid type, completion brine, and shale inhibitors play an important role in maintaining well integrity and preventing formation damage as they have different effects on shale.
Reactivity of shale samples from offshore Gulf of Mexico was studied using several reservoir and completion fluids. The shale was first characterized using X-Ray diffraction (XRD) and X-Ray fluorescence (XRF). Then, the effect of fluids on shale was determined by performing several tests including linear swell meter (LSM), capillary suction time, and cuttings dispersion. Water-based (WB) and oil-based (OB) drill-in fluids, synthetic formation water, and completion brines with and without shale inhibitor were used to study the shale reactivity.
XRD and XRF test showed presence of 5wt% illite and 6wt% kaolinite in the shale sample. Performance tests conducted on the shale samples showed a similar trend of high reactivity with improvements when inhibitor is added. Some brines showed poor synergistic effects when inhibitors were present in the fluid formulation. The type of drill-in fluid has a large impact on the LSM test results. Type of brine in the WB drill-in fluid also showed a major influence on the shale behavior.
Oil-based drill-in fluids are commonly used to drill sections with very reactive shales. Nevertheless, water-based fluids are sometimes required due to performance preferences, environmental concerns, economic and logistic reasons, and/or synergistic effects with logging tools. Filtrate invasion and drill-in fluid/completion brine losses could cause detrimental effects in the reservoir if the fluids have not been designed to inhibit the shale hydration and swelling or fines migration. Therefore, improving the clay inhibition and shale stability while using water-based fluids is fundamental when drilling through reactive shale sections. Performing a comprehensive test matrix to determine the feasibility of using water-based fluids is imperative.
Achieving excellent inhibition for a very reactive GOM formation shale while maintaining performance is possible when a precise and well-engineered combination of brine/shale inhibitor in the drill-in and completion fluids are found.
Integrated Pressure Activated Valve with Autonomous Inflow Control Valve Technology Minimizes the Deployment Risks in Challenging Well Control Conditions
Authors: Abd El-Fattah Mahmoud , Nomme Christian , Werswick Bjørnar 
Published: February 2022

Abstract
In heavily fractured carbonate reservoirs, wells often suffer heavy losses while running lower completion. To avoid any unexpected flow-back, continuous pumping is required. Failure to do this will result in flow-back of the reservoir fluid into the completion string and could reach the surface, leading to a major well control risk. Integrated Pressure Activated Valve (PAV) with Autonomous Inflow Control Valve (AICV) offers a unique solution to manage well control risks, allowing safe deployment of lower completion in such challengeable situations.
The PAV module is mounted in series between the screen and AICV housing and will be a part of a lower completion. PAV-AICV system remains closed while RIH, ensuring no fluid can flow through it in either direction. Once the completion reaches the target depth and other pressure-activated completion tools are set/tested, then the PAV is triggered by applying the design pressure in the tubing. PAV will open once activated pressure is reached, and open when the pressure is released, establishing the flow path between the reservoir and tubing via AICV.
The qualification test of the AICV-PAV integrated system involves multiple functions and pressure tests. The test results prove the reliability and robustness of the PAV to get activated at the design pressure window. This paper will describe in detail the operating principle along with the testing results of the AICV-PAV system. During the deployment of completion, the AICV-PAV system acts as a well barrier between the reservoir and the completions system and prevents reservoir fluid to enter the completion string. This will result in reducing the well-control risk significantly while deploying the completion string, especially in a fractured or faulted reservoir. Furthermore, this system allows all formation treatment fluids to be fully circulated through the end of the completion string, thus eliminating the requirement of an inner string. Furthermore, it also allows setting liner hanger packer and hydraulically set mechanical packers. This will result in saving rig time in running extra runs and further reducing cost/risks as well as increasing operational efficiency. Once the PAV module is open, it stays in an open position and normal AICV functionality is established. The system is field ready and will be deployed for a major operator in the Middle East.
PAV module incorporated with AICV provides pressure integrity in the liner for lower completion installation. This ensures an optimized lower completion installation in challenging situations, especially with well control issues. This paper will describe the newly developed AICV-PAV system operating principle, functionality and discuss in detail the various testing results.
Automatic Neural Network-Based Seismic Facies Classification Using Pseudo-Labels
Authors: Tolstaya Ekaterina , Egorov Anton 
Published: February 2022

Abstract
In this paper we propose a method of seismic facies labeling. Seismic facies labeling task consists of assigning specific geological rock types to the pixels in the seismic cube. In our research we use open-source fully annotated 3D geological model of the Netherlands F3 Block. The dataset is divided into training and test cubes. We use the former to train a state-of-the-art deep learning neural network, adding a 3D conditional random field (CRF) layer as a postprocessing step. We apply the pseudo-labeling technique, where the labels of the test dataset are predicted and added to the training set to get more accurate final prediction. To diversify the training dataset, we also apply different types of augmentations, including a domain specific image warping technique. Using the trained network, we predict the facies labels on the test dataset and compute various metrics. The results suggest superior network performance over the existing baseline model.
Digital Revolution Provides a Step Change in Efficiency for Core Testing and Reservoir Upscaling for Acid Treatments
Authors: Ivanov Evgeny , Khan Abdul Muqtadir , Yudin Alexey , Stukan Mikhail , Khan Vladimir , Dovgilovich Leonid 
Published: February 2022

Abstract
This paper presents a digital stimulation workflow intended to revolutionize the area of tedious and core-destructive acidizing experiments. To adjust acid treatment design with respect to actual geology and lithology, several acidizing tests must be performed on cores taken from the reservoir of interest. However, complications related to core sampling and experimental uncertainties often make such tests infeasible, since the adjustment is based on "equivalent" data, which inevitably affects the treatment performance.
A better result is achieved by supplementing laboratory tests with numerical simulation of reactive Darcy flow on digital avatars built using X-ray microtomography of real cores. Direct pore-scale simulations using high-resolution micromodels can help determine porosity, which in turn yields values for rock permeability, active surface area, and effective reaction rate. These data are then used to populate the standard core-size Darcy models used to simulate the rock dissolution at different injection rates. At the final step of the workflow, the obtained correlation of rock dissolution versus injection rate are used in advanced numerical simulators for well-scale modeling, in order to optimize the matrix-acidizing (MA) and acid-fracturing treatments.
In this study, the concept was demonstrated by dissolving the limestone core in hydrochloric acid. The first phase of the workflow aims at digital expansion of the conventional laboratory work, which results in significant optimization of laboratory resources. Based on X-ray microtomography (microCT) scans of just two core samples and three laboratory filtration tests (one trial and two for the reference), by means of extensive cloud-based simulations, the authors were able to build two full-range pore-volume-to-breakthrough (PVBT) curves that are commonly used for treatment design. The second phase of the workflow involves reservoir-scale modeling and optimization of the acid treatment in an advanced digital simulator. A sensitivity study for the second-phase conceptual workflow was performed under typical well conditions in Middle Eastern basins to calculate volume and injection rates for optimum wormholing. Engineered design strategy going beyond conventional rule-of-thumb practices showed that significant production enhancement can be achieved while optimizing the acidizing project economics.
Using a digital workflow makes it possible to optimize carbonate acidizing specifically for each well condition. Digital twins of the core samples and acid systems allows multiplying the number of filtration experiments by orders of magnitude, which significantly improves final data quality. Digital workflow is applicable to any carbonate reservoir using minimal core materials and lab resources.
Data Driven Intelligent Modeling to Estimate Adsorption of Methane Gas in Shales
Authors: Kalam Shams , Abu-Khamsin Sidqi A. , Khan Mohammad Rasheed , Abbasi Asiya , Asad Abdul , Khan Rizwan Ahmed 
Published: February 2022

Abstract
Artificial intelligence is a smart tool widely used in Petroleum engineering. Adaptive Neuro-Fuzzy Inference System (ANFIS) is an artificial intelligence technique that is a hybrid between Artificial Neural Networks (ANN) and fuzzy logic. In this paper, both ANN and ANFIS were applied to propose a new methodology based on intelligent algorithms to predict adsorption of methane gas in shale. Feed-Forward Neural Network and subtractive clustering were applied to correlate adsorption with several parameters. These include temperature, pressure, moisture content, and total organic content (TOC).
A real data set collected from the literature, which includes about 350 data points, was used in the development of a new empirical correlation. The set was divided into a 70:30 ratio for training and testing, respectively. The average absolute percentage error, correlation coefficient, and mean squared error were considered in the error metrics to obtain the best possible model.
The results show that methane adsorption can be efficiently correlated with the inputs using both machine learning tools. Using ANN, the correlation coefficient for both testing and training data was more than 99%. A detailed sensitivity analysis for the ANN model is also provided in this paper.
Extended Abstract: The Feasibility of Using Geopolymer in Oil-Well Cementing: A Review
Authors: Adjei Stephen , Elkatatny Salaheldin , Aggrey Wilberforce Nkrumah , Abdelraouf Yasmin 
Published: February 2022

Abstract
Over the years, various cementitious materials have been investigated as a substitute for conventional cement. One example of these materials is geopolymer, a binder developed when an alkaline solution is used to activate materials containing alumina and silica. The use of this material is well established in the construction industry. In oil-well cementing, its feasibility is currently being investigated.
An extensive survey on the various geopolymer studies has been conducted. The goal is to present a manuscript containing a summary of these studies. This will help researchers merge the knowledge acquired going forward.
The study showed that the application of geopolymer in acidic and saline conditions, and in well plugging and abandonment operations. Additionally, geopolymer-mud compatibility and the impact of temperature on geopolymer systems have also been studied. In general, geopolymer systems show better performance, overcoming the limitations of the OPC systems. For instance, the geopolymer is more suited for CO2 sequestrations wells as it does not undergo a carbonation reaction which would result in degradation. Furthermore, geopolymers have superior performance in highly saline conditions and besides their compatibility with mud, a geopolymer-mud combination produces cementitious systems with enhanced properties.
Drilling Data Quality and Reliability: A Novel Algorithm for Data Correction and Validation
Authors: Koulidis Alexis , Ooi Guang , Skenderija Jelena , Ahmed Shehab 
Published: February 2022

Abstract
The difficulty of drilling data analysis stems from the fact that such data often contain errors and disturbances such as missing samples, desynchronization, misinterpretation, ambient noise, and unfit equations and models. In this study, an algorithm is developed to identify different operations during the drilling processes and automate the procedure of validating data quality.
The proper analysis and processing of drilling data is crucial in ensuring its quality. Initially, the algorithm separates drilling data reported in 5 sections from the Equinor Volve dataset into different intervals depending on their mode of operation. Additionally, the algorithm performs data correction and provide analysis on data quality. The data is corrected based on a series of physics- and operation-informed conditions. Key performance indicators (KPIs) are calculated for the rate of penetration (ROP), weight on bit (WOB), bit depth, measured depth, and torque. The KPIs include validity, accuracy, and Kuder-Richardson 20 (KR-20) scores. The KR-20 score, which is a measure of the lower bound of the reliability of data, is selected since it considers the difficulty of measurement of each attribute to be unequal, i.e. some attributes may be more prone to error than others.
Based on the results, the corrected data displays better correlation between the aforementioned drilling data. The results prove that the intelligent software analysis provides an automated workflow that allows the separation of all operations and the required time in addition to the nigh instantaneous generation of corrected data and data uncertainty qualification.
The detection of invalid data points was performed by investigating every operation i.e. rotating off bottom, drilling mode and evaluate the corresponding measurements. The analysis showed that approximately 76% of ROP values are invalid data, which validates the importance of data correction in order to be used for validation.
The developed algorithm allows the rapid and reliable analysis of unprocessed drilling data to facilitate decision making and quality control. The key advantages of the intelligent algorithm are that it provides fast data assimilation and comprehensive analysis of drilling parameters by allowing rapid visualization and quantification of data quality and reliability.
The correlation of drilling parameters for various operations can be evaluated and visualized with correlation matrices (heatmap). The primary qualification from the original set of data shows a low correlation i.e. rate of penetration values on rotating off bottom operation, which indicates low data validity. The current method and procedure show a significant correction of data point correlation from −1 to up to 1 depending on the operation. More importantly, for a specific set of the original data, ROP showed a validity of 0.23 in contrast 0.75 for bit depth and with accuracy of 0.58 and 0.99 respectively.
Production Optimization of a Large Network of Oil Wells with Electrical Submersible Pumps as the Artificial Lift System
Authors: Roy Arjun , Datchanamoorthy Senthilkumar , Sharad Nagappa , Nundy Sangeeta 
Published: February 2022

Abstract
One of the primary tasks of a production engineer at an oil field is to maximize oil production from a field comprising multiple wells, while respecting a multitude of constraints related to operational limitations of components, component reliability, interaction between wells, environmental concerns, and operational costs. This is a multi-objective multi-constraint problem involving multiple physics models that interact with each other. Further, the total number of optimization parameters and constraints grows linearly with the size of the field. This makes the problem computationally intensive for oil fields with hundreds of wells and thus the direct use of a standard optimization algorithm will be inefficient. This paper describes a computationally tractable and scalable approach to solve this problem.
Numerical Investigation for Developing Conventional Tight-Oil Formations in the Middle East
Authors: Althani Mohammed , Ozkan Erdal 
Published: February 2022

Abstract
The rise in oil production in the United States during the last decade resulted from the development of unconventional tight-oil resources. These are oil accumulations in low permeability formations, which are either source rocks or in a proximity to a source rock. There are other types of tight-oil resources that are located in conventional reservoirs with a low permeability matrix. These can be referred to as conventional tight-oil formations. The objective of this paper is to investigate the characteristics of the development strategies for these formations and how they are compared to unconventional tight-oil resources.
Using reservoir data representing the properties of the tight-oil formations in the Middle East, a numerical study has been conducted by a commercial simulator with dual-porosity option. The model can simulate the drilling and the hydraulic fracturing in the reservoir. In addition, the model is coupled with geomechanics to capture the stress changes in the formation. The effects of the natural fracture corridors and water production from nearby aquifers on the performance of conventional tight-oil formations are also studied. Sensitivity analysis is conducted on reservoir data, such as the initial reservoir pressure and initial movable water saturation.
The simulation results show that, as in the unconventional tight-oil plays, horizontal wells with hydraulic fractures are the best approach to develop conventional tight-oil formations. However, wider spacing of these horizontal wells is warranted if they are intersecting the natural fracture corridors. High water production from nearby aquifers or high initial water saturation is a major challenge and could jeopardize the development outcomes. In addition, lower initial pressure compared to unconventional tight-oil plays may undermine the initial rates, and, unless the matrix permeability is high enough for sustained economic rates, conventional tight-oil resources may not have some of the favorable features of unconventional tight-oil project economics.
This paper illustrates the factors affecting of the development strategies for conventional tight-oil formations. Most of the studies available in the literature focus on the development of conventional high/medium permeability formations or unconventional tight-oil formations. This paper provides insights into the development for a different category of oil resources that has a high potential in the Middle East and other parts of the world.
Petrophysical Evaluation and Geochemical Characterization of Abu Roash F Member Abu Gharadig Basin, Western Desert, Egypt
Authors: Farrag Sayed , Mahmoud Ihab 
Published: February 2022

Abstract
Abu Roash F Member (ARF) is a carbonate formation extended all over the Western Desert in Egypt, ARF has good lateral continuity in all western desert basins but has very poor connectivity and very low permeability. it can be considered as an example of unconventional reservoir. This work aims to evaluate the reservoir quality of Abu Roash "F" Member and compare with the unconventional play commercial developed all over the world. in this study, the key parameters to define reservoir quality; include mineralogy, porosity, water saturation, permeability, organic matter content, Kerogen type and thermal maturity has been investigated. More than 30 Rock-Eval pyrolysis samples from different fields where ARF at significantly different level has been used to evaluate and understand ARF geochemical reservoir quality. On the other side, core and well log data from different fields at different level has been inspected and integrated to evaluate ARF mineralogy, porosity, permeability, water saturation, and identify Potential sweet spots. The results of Rock-Eval analysis show that most of the investigated samples have the total organic carbon content (TOC) values between 1.6 and 6.63 wt% indicating good to very good source rocks and the pyrolysis Yield (PY) ranged from 6 to 20 indicating good to very good potential generation. Based on Tmax and Hydrogen index (HI), the deepest well samples have Tmax values in the range of 435 and 441°C and Hydrogen index (HI) values in the range of 311 to 570 indicating that the organic matter has reached the early to intermediate stages of thermal maturity with dominate kerogen type I-II. While the shallower well samples have Tmax values in the range of 421 and 430°C and Hydrogen index (HI) values in the range of 127 to 687 indicating that the organic matter immature with mixed kerogen type II-III.
Petrophysical results supporting that ARF is a carbonate rock deposit under marine conditions and has mixed layer clay (montomonlionite, Kolonite and illite). Numerous techniques to estimate ARF permeability from wireline logs have been investigated, using the available core data porosity permeability relationship has been established. Moreover, the results of petrophysical analysis indicate that Lucia class 3 permeability has good math with core permeability. So, Lucia class 3 permeability can be used to estimate ARF permeability using the calculated effective porosity from well log data.
Generally, the results of geochemical and petrophysical evaluation of this study show that ARF has very good reservoir quality comparing with the most of commercially developed unconventional resources all over the world. Moreover, the results show that ARF has a similarity with Eagle Ford Shale in terms of Age, mineralogy, pressure, depth, thickness, and TOC which reflect the potentiality of ARF commercial development.
Process Simulation to Design Dehydration Systems with Separators and Inline Glycol Injection Segment
Authors: Mitra Purnayan , Kalia Isshita 
Published: February 2022

Abstract
Raw natural gas contains methane, ethane, propane, some amounts of heavier hydrocarbons and impurities like water vapour, sulphur compounds, carbon dioxide, nitrogen etc. Presence of these components along with methane reduces the calorific value of the natural gas. Also, high percentage of impurities in natural gas can block pipelines due to hydrate formation during transportation. Therefore, to meet the pipeline specifications, natural gas dehydration plants are used to lower the percentage of water in it. Dehydration of natural gas can be carried out with the help of the adsorbents as well as absorbents. Choosing a dehydrating agent and designing of dehydration plant depends on a number of factors. Gas flow rate, percentage of initial and final water composition, dew point depression attained, and cost are some of the important factors to be considered during designing of dehydration plant. In this paper, we highlight the simulation of a natural gas dehydration process which has an inline glycol injection system and then compare its efficiency with the conventional dehydration system. The cost of setting these two units is also compared. The simulation study shows that the glycol injection system significantly reduces the mole fraction of water in the natural gas.