bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning to Detect Oil-Based Mud Contamination in Oil Samples
Authors: Almalki F. F. , Alotaibi M. D. 
Published: February 2024

AbstractCurrent methods to decontaminate bottom hole oil samples from Oil-Based Mud (OBM) poses many challenges as it requires analytical solutions. The existing analytical practices are highly subjective, as contamination results can differ depending on the approach determined by the user. The proposed method involves training a newly developed machine learning model to seek contamination patterns in an oil or gas condensate sample and highlight the contaminated samples. A python-based program that utilizes machine learning techniques to predict the concentration of OBM contamination in hydrocarbon fluids was constructed. The proposed procedure involved collecting 397 records of analyzed liquid C1 – C44 weight percent and whether they were reported as contaminated with OBM or not. The records were normalized and standardized on a column-basis, to preserve the confidentiality of the data. The model then, was trained on different algorithms, then was subjected to real cases to determine the model’s prediction accuracy. The train/test ratio was chosen to be 80/20 across different algorithms. The model was subjected to different algorithms. The algorithms were logistic regression, manual logistic regression, linear support vector machine (SVM), kernel SVM, Decision Tree, and Random Forest. The Manual Logistic Regression (MLR) algorithm resulted in an accuracy of 97%, and an F-1 Score of 94%. This shows that the MLR algorithm is the most suitable to be used in this prediction scheme. Moreover, this algorithm can be used as a quick method to get a sense of whether the fluid is contaminated or not. This illustrates the model’s significance in OBM contamination detection and making it the tool to be used in deciding the validity of a hydrocarbon sample. The current methods that are used to analytically decontaminate a fluid are based on analytical approaches, which depend on the person interpreting the results. Machine learning will resolve the issue of subjectivity in determining the contamination existence, increase the efficiency of the process, and is one step forward towards digital transformation.
Predicting Mineralogical Composition in Unconventional Formations Using Machine Learning and Well Logging Data
Authors: Gainitdinov Batyrkhan , Meshalkin Yury , Orlov Denis , Chekhonin Evgeny , Zagranovskaya Julia , Koroteev Dmitri , Popov Yury 
Published: February 2024

Abstract
Quantitative determination of mineralogy can be done using high-definition spectroscopic logging methods, however these methods are rarely used due to complexity and cost. Also, it is difficult to obtain mineralogical composition in unconventional formations due to presence of kerogen and high heterogeneity and anisotropy of such formations. This problem can be resolved by utilizing Machine Learning algorithms based on well logging and thermal profiling data which can improve and speed up reservoir characterisation. Special wrappers such as Multioutput Regressor and Regressor Chain were applied to test several machine learning models and strategies of well logs combinations on multiscale data from an unconventional formation in West Siberia to predict mass and volumetric fractions of minerals obtained from Litho Scanner. RMSE and MAE were used as regression metrics. To validate the results, theoretical model was used to calculate thermal conductivity based on mineral volume fractions and compared with experimental data. Regressor Chain showed better performance for weight fractions prediction when data was scarce. The Gradient Boosting Regressor encapsulated within a Regressor Chain exhibited the most favorable outcomes in relation to the precision of mapping. The evaluation contrasting the ML-based model with the LithoScanner exhibited an average discrepancy of 0.026, as measured by the RMSE metric.
Role of Ecosystem Partners to Make Nonmetallic Downhole Tubulars a Reality
Authors: Aladawy A. , Malkawi A. , El Shamy O. 
Published: February 2024

AbstractNonmetallic downhole tubulars offer a longer lifetime alternative than their steel counterparts while eliminating corrosion concerns and lowering total cost of ownership. However, making them a reality requires a rigorous ecosystem with multidisciplinary skill sets and technology expertise.This paper discusses the ecosystem challenges that face the development of such tubulars starting from academia and research institutes to complement expertise and computer computational power for design, modeling, and simulation, to material suppliers and manufacturing facilities for the pipe prototyping. This study also includes testing houses and standardization organizations for the pipe testing and qualification program as well as the needed logistics for transportation and storage, ending with the system implementation service providers for the qualification, deployment, operation, and retrieval of the downhole tubular systems.A detailed workflow for the integrations of the required ecosystem as well as examples are explained, and light is shed on the essential industry partnerships to make it happen. Focusing with examples on the Middle East region, multiple gaps need targeted localization of multidisciplinary capabilities, establishment of third-party startups and nourishment of small and medium enterprises, joint ventures, and joint industry programs. Specific focus was shed on the role of different ecosystem partners including pipe manufacturer, service provider, well operator, material suppliers, software developers, academia & research institutes, test houses, standardization institutes and logistics providers. Their contribution through the different phases of development, installation, lifetime, and retrieval of composite tubulars was described.This publication is believed to be the first that tackles such complex problem with a holistic overview and way forward to solve. With such recommendations, nonmetallic downhole tubulars would be made possible with the proper ecosystem in place.
Experimental Visualization of Downhole Drilling Vibration Using Industrial Drilling Dynamic Recorder
Authors: Maitra E. K. , Al Dushaishi M. F. , Sugiura J. , Jones S. 
Published: February 2024

Abstract
Housing miniature sensors within bottomhole assemblies (BHA) have proven to provide crucial real-time and valuable post-well analysis for drilling dynamics optimization and vibration mitigation. The downhole dynamic behavior of the BHA is often misinterpreted and requires special data preprocessing for effective optimization. The objective of this work is to establish an understanding and standardization of downhole vibration data analysis using a scaled laboratory BHA equipped with a high frequency industrial drilling dynamic (DD) field sensor and trajectory mapping station. A novel downscaled test assembly adhering to the geometric and material property relations of a field-size BHA section was designed and manufactured to investigate the nature of lateral vibrations. Axial excitation, i.e., weight on bit (WOB) fluctuation, and rotation were induced respectively using an electromagnetic shaker and an electric motor. A downhole drilling dynamic recorder was housed near the bit-end. BHA trajectory was mapped using inductive displacement sensors at multiple locations, along its length, to test the capability and accuracy of the DD field recorder. The wellbore structure was built with transparent material for direct visual correlation between the BHA movement and the drilling dynamic recorder. The results from this experiment provide precise and insightful visual information for the BHA movement correlation with the dynamic data acquired from a continuous high-frequency downhole field sensor. Real-time induced axial WOB fluctuation was recorded using a high-frequency drilling dynamic recorder and the induced axial force was distinctly addressed with the level of magnitude and frequency. The BHA vibration behavior of high-frequency accelerations, critical lateral vibration phenomena such as whirling motion, angular velocity (RPM) responses, and vibration behavior shift at the harmonic frequencies were characterized in relation to both the nature of induced forces at the bit and the acquired downhole field sensor data. The novel experiment establishes both quantitative and qualitative relations between DD data and physical vibration behavior during drilling. The experimental results showed the benefit of drilling dynamic recorders and it is accurate in predicting severe lateral vibrations while for the first time providing visual representation to aid in vibration data interpretation.
A Systematic Approach to Drilling Performance Improvement and Risk Reduction in Horizontal Wells
Authors: Wang Xiaoliang , Zhou Bao , Wen Liang , Qin Hao , Yang Junqi , Qin Guangming , Guo Xiaosha , Wu Yangsong , Huang Xingning 
Published: February 2024

Abstract
The Fuman oilfield is the main block of the Tarim oilfield to increase reserves and production. However, the wells in this area are ultra-deep, and many sets of complex and difficult-to-drill formations are encountered in top-down drilling, which brings significant challenges to the drilling engineering. Based on triaxial and shear experiments, transverse isotropy theory and weak surface criterion, and integrating the mechanical damage under stress unloading and hydration in drilling wells, a well wall stability model under the synergistic effect of stress unloading and hydration has been established, and an analysis of the factors influencing the stability of the well wall of the formation has been carried out. The study shows that stress unloading occurs after drilling. The greater the confining pressure and axial stress, the more significant the shale strength weakening caused by stress unloading, and the magnitude of stress unloading is positively correlated with the degree of mechanical strength weakening. The more developed the laminations are, the easier it is to damage along the laminations. When stress unloading occurs, the weak structural surface acts synergistically with stress unloading, and the higher the degree of shale strength weakening is, the worse the mechanical stability is and the higher the risk of well wall instability. External fluids are prone to intrude along the bedding and promote shale hydration, making the shale mechanically less stable and reducing the well wall's stability. The effect of stress unloading on shale formation collapse pressure mainly occurs in the pre-drilling stage, and the effect of hydration damage on well wall instability mainly occurs in the late drilling stage. Beddings, stress unloading, and hydration affect the well wall stability together. The shale well wall stability model under stress unloading-hydration synergistic effect considers the influence of the three comprehensively. The prediction results align with the drilling actuality with good reliability after the field application test.
Erosion Mitigation During Testing of High Deliverability Gas Well - A Case Study
Authors: Bashir I. A. , Inam S. , Sana-ul-Hussnain M. , Shafee I. , Aijaz M. , Ullah R. , Haider M. , Qureshi H. A. , Khan M. , Ahmad H. , Awan F. R. 
Published: February 2024

Abstract
The paper details the case history of a highly challenging, high deliverability exploratory gas well that was successfully drilled and completed in the frontier region. The 4,900 m well encountered multiple reservoirs during drilling with severe losses in the topmost prospective limestone reservoir formation. Subsequently, well was cased and Drill Stem Test (DST) was performed to test reservoir deliverability. During testing, severe leakages were encountered at the Surface Well Test (SWT) equipment due to high flow velocities, coupled with Loss Control Material (LCM) and formation cuttings flowback. This led to internal erosion in the equipment, resulting in frequent uncontrolled hydrocarbon release into the atmosphere, posing a high risk for personnel. Consequently, frequent shut-ins for maintenance were necessary. Steps taken to mitigate this issue are discussed and recommendations are proposed for future encounters.
A systematic approach was adopted to offset the effects of erosion and consequential leakages. Points were identified which were prone to leakages, and successively, steps were taken to minimize bends. Non-intrusive thickness monitoring was carried out regularly to detect reduction(s) in the pipe schedule preemptively and take appropriate measures. Erosional velocities were computed for different pipe sections and sufficient back pressure was applied to reduce flow velocity. Other measures such as reducing flowing times at higher chokes and acid stimulation were also carried out to minimize the impact of, and dissolve, the particles eroding the equipment.
Significant improvements were observed after taking preventive measures – Non-Productive Time (NPT) was reduced, and the remaining job was carried out safely without any impact on the personnel, equipment, environment and the Company reputation. Accuracy of the collected data also improved, as particles had inadvertent effects on flow measurements.
An extensive study was carried out followed by on site actions to reduce the erosional impacts on a high pressure, high deliverability gas well. Important conclusions were drawn. Safe practices were developed to avoid future occurrences in high volume wells. A combination of steps such as re-arrangement of SWT setup to minimize bends, monitoring pipeline thickness using ultrasonic thickness meter, minimizing flow duration at high chokes, calculating downstream pressures required to limit erosion and applying back pressure, and performing acid stimulation can all help future operations to be carried out safely without any untoward downtime. In addition, dissolvable / degradable LCM could be opted in future wells, and wellbore hydraulics could be further studied to ensure optimum hole cleaning for effective cuttings removal.
Multilayer Perceptron Modeling for the Prediction of Gas Condensate Composition Changes During Pressure Depletion
Authors: Embaireeg Aisha 
Published: February 2024

Abstract
The production of the gas condensate in addition to the gas itself can improve the recovery factor of gas condensate reservoirs, as well as increase the economic feasibility of the reservoir. Hence, the prediction of the variation of the fluid properties of gas condensate reservoirs requires an accurate study of reservoir fluid characteristics during various pressure stages. The changes in produced gas composition during production are usually obtained from Pressure-Volume-Temperature (PVT) tests such as the Constant Volume Depletion (CVD) tests, provided the sample is available. Therefore, this study aims to develop an intelligent model to anticipate these changes in gas condensate composition during depletion stages, especially in cases where sample availability or reliability is limited.
A large data bank comprising of CVD tests for 141 of gas condensate samples at various pressures, temperatures, and compositions has been used to develop a multilayer perceptron neural network model to estimate the depletion changes in gas condensate composition from its initial composition. Furthermore, in order to demonstrate the effectiveness of the proposed model in cases where the Equation of State (EOS) is not accurately calibrated, a comparison was conducted between the proposed model and the EOS for all gas condensate samples.
The statistical error analysis show that model has high degree of accuracy to predict the gas composition within three depleted pressure stages. The model produced an average absolute percent error (AAPE) of 19.55% and 9.27%, and a coefficient of determination (R2) of 0.953 and 0.970, for sweet and sour gas condensates, respectively. Therefore, the model has an excellent agreement with the experimental data. All gas condensate samples in the data bank which were used to develop the model have also been used to study the accuracy of the Peng-Robinson Equation of State (PR-EOS) in comparison with the proposed model. The errors of the PR-EOS prediction of changes in the produced gas condensate composition resulted in an AAPE of 15.68% and 19.18%, and R2 of 0.936 and 0.828, for sweet and sour gas condensates, respectively. The accuracy of the proposed model in predicting the changes in the produced gas condensate composition is favorable in comparison with PR-EOS. In addition, the model eliminates uncertainties related to splitting and characterization of the plus fraction and inclusion of the binary interaction parameter correlations which are required for EOS.
The neural network architecture used in the proposed model exhibits a remarkable similarity to the experimental data. Furthermore, the model is highly practical, as it can be implemented within a programming language to rapidly estimate the gas condensate's compositional variation at different pressure stages.
Natural Fracture Modeling in Unconventional Dadas-I Member for 3D Seismic Survey in the Southeastern Turkey
Authors: Orlov Artem , Özkul Canalp , Bensenouci Fethi , Yazaroglu Ekrem , Mengen Ahmet Ergun , Yilmaz Ismail Omer 
Published: February 2024

Abstract
Silurian age Dadas-I shale exhibit low porosity and extremely low matrix permeability. Acting as one of the main source rocks in South-East Turkey, it represents a self-sourced unconventional play. This study aims at using an approach spanning from seismic to image log data analysis and 3D geomechanics to develop a discrete fracture network model (DFN) to provide new insights into distribution of hydrocarbons since only developed natural fractures make this reservoir producible.
The study uses borehole image log data acquired in the Dada»-I interval of 3 exploration wells: X-1, X-6, X-9, 3D seismic and 3D geomechanical model. The main steps of the workflow are: 1).to use interpreted image logs and classify the fractures by fracture sets, 2).to evaluate possible fracture drivers, defined as any 3D properties that can be sensitive to fracture intensity information in the inter-well space, 3).to review this analysis in the context of the seismic structural interpretation and regional tectonic framework, 4).to employ geomechanical model for likelihood of local reactivation.
The analysis of natural fracture properties, together with petrophysical data, seismic interpretation, and stress regime enable to build a 3D DFN model, predicts fractures occurrence in the Dada»-I interval and used this output for dynamic reservoir modeling. The seismic attributes together with regional geology reveals two major sets of fractures: strike in ESE-WNW that relates to the first extensional tectonic event and the second with a strike NNE-SSW after paleo-stress rotation from E-W to N-S. Application of geomechanical model discloses that not all fractures should be considered as point of weakness. Only discontinuities that are favorably oriented to in-situ stress more likely to be hydraulically conductive. The result of this interpretation were used to predict hydrocarbon flowing zones. Accordingly, the selection of testing intervals on the basis of the highest density of fractures only is not a valid approach to determine prospective zones. In the current strike-slip/compressional regime, the fracture direction and dip is the main control to hydrocarbon flow with the best contribution from natural fractures oriented close to the present day maximum horizontal stress.
Effects of Vibrator Distortion Induced by Surface Lithology on Seismic Data
Authors: Zheng Jie , Sun Junhe , Meng Qingbin , Peng Ai , Lyu Shuai , Ma Fei 
Published: February 2024

Abstract
Evaluating the effect of vibrator distortion on seismic data is not straightforward, particularly in terms of how to control the high distortion caused by surface properties, which can affect progress if it results in unnecessary repeats of sweeps. A series of vibrator tests for 25 days were designed and carried out, including comparative tests on different terrains and surfaces, and all relevant data was collected, including but not limited to: wired similarity tests; wireless similarity tests; sandwich-box tests; VAPS data; and recorded raw sweeps. After analysis of the causes of high distortions, raw sweeps, and the stack, it was concluded that the impact of the distortion on the seismic data could be neglected. The final conclusion is therefore that peak distortion should not be used as a parameter for quality control. Nevertheless, if the average distortion together with the average force is more than 100%, this means that the source parameters are perhaps not suitable for the type of surface in the work area, and the force level may need to be reduced. The even harmonic is mainly caused by near-surface properties. As long as the quality of the recorded data is not affected, minor fluctuations in certain parameters should not immediately lead to the conclusion that the vibrator is faulty, but should continue to be closely monitored. Improved field quality control of vibroseis units will reduce unnecessary delays in production as a result of distortion measurements.
Enhancing Production Efficiency by Unloading Produced Water and Condensate Using Engineered Surfactants: Case Histories
Authors: Omer Mohammed , Busteed John E. , Fragachan Francisco E. 
Published: February 2024

Abstract
Due to the depletion of reservoirs, liquid loading has become one of the common issues in mature fields. It happens where gas rates are not sufficient to lift associated reservoir fluids to the surface. Foaming surfactant injection is one of the methods used to mitigate liquid loading and enhance stable hydrocarbon production. In foam-assisted lift, a well is offloaded by creating foam downhole by the injection of surfactants. Surfactants reduce the liquid surface tension resulting in foam creation when the gas mixes with water or condensate downhole. This results in reduced liquid density and reduces hydrostatic pressure, thereby increasing drawdown and increasing resulting production.
We have developed a method to design and evaluate foaming surfactant performance for unloading water and for unloading condensate. This method eliminates the potential to create stable emulsions (through high mechanical shear). Additionally, the methods outlined will avoid misleading interpretations where only the foam half-life has been considered. Falsely optimistic results, from a foam half-life perspective, can occur where certain surfactants result in "weak" foams that can maintain volume while "dropping" a large fraction of the "liquid" out of the foam. In this method, foam density, or foam mass versus time, is considered.
The study results in an evaluation through the measurements of the time to create a known volume of foam, the rates of foam dissipation, and liquid phase recovery rates. This analysis is done by plotting foam multiple parameters versus time. Finally, the observation of individual phase attributes is carried out.
This paper will introduce a unique methodology, process, and design to evaluate foaming surfactants for condensates and for produced water. This paper will also present the findings from foaming surfactant applications for unloading produced water in the Middle East, Asia, and unloading condensate from wells in Europe. This work will help in optimizing the design for foaming surfactants and, hence, help enhance hydrocarbon production.