bluuebunny/crossref_metadata_2025_split huggingface dataset:
Next-Generation Drilling Fluid Management: Real-Time Monitoring and Autonomous Control
Authors: Zhang Z. , Rached R. M. , Xun Y. , Tian J. , Wang Z. , Fan S. 
Published: October 2023

Abstract
Accurate measurement and control of drilling fluid properties are crucial for safe and successful drilling operations. Traditional manual methods are slow and require significant manpower, underscoring the need for real-time monitoring. This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling fluid performance. The online monitoring system improves drilling industry safety and success by measuring accurate, timely, and automated properties of drilling fluids.
The online drilling fluid monitoring system continuously measures ten essential parameters, including apparent viscosity, plastic viscosity, yield point, density, pH, and ion concentrations. The system uses a newly-developed variable diameter pipe technology to measure viscosity, and a patented double-pressure vibrating tube technology to measure density. Ion-selective electrodes enable fast and accurate ion concentration measurements, eliminating cumbersome titrations. The novel methods provide a quick measurement (&lt;1s sampling rate) with a wide range and minimal errors. The system is modular and includes sampling, measurement, data acquisition, control, and pressure protection subsystems, with software integration for data visualization and remote data transmission to predetermined data centers.
The system achieves one measurement every second, providing real-time and continuous monitoring of 10 essential parameters. With 30 equipment sets produced and trialed in 200 wells, the system has demonstrated continuous operation for over 125 days. The online, real-time, and continuous monitoring capabilities offer several benefits, including improved data quality and frequency, reduced on-site labor and associated health and safety hazards. Notable field applications of the system include the automatic detection of the mud density anomaly in a critical well with a narrow safe mud density, and remote real-time transmission of data for integrated geological logging analysis. Further development will focus on automating error detection and incorporating additional sensors for multiphase fluids.
This paper highlights a new online drilling fluid monitoring system that improves safety and success in drilling by providing accurate, timely, and automated performance control. The system measures ten essential parameters utilizing patented technologies and provides a continuous profile of the full set of properties with time. Field trials have demonstrated the advantages of this technology, including remote data transmission and automated error detection. The system reduces on-site labor and associated safety hazards, improves HSE on-site, and opens the door to oilfield data analysis automation.
Enabling Contextual Natural Language Search on Oil and Gas Databases
Authors: Lokhande Avinash , Pillai Prashanth , Mangsuli Purnaprajna 
Published: October 2023

Abstract
The oil and gas (O&G) industry generates a significant volume of data from various sources, such as seismic surveys, well logs, drilling reports and others, which are stored in either relational or non-relational databases. However, searching for pertinent data records requires the end-user to be conversant with database query syntaxes and schema definitions, which is challenging. In this paper, we introduce a novel framework to interact with O&G databases using natural language. We trained a Text-to-Text Transfer Transformer (T5) (Raffel, 2020) in a multi-task setting incorporating natural language to structured query language (Text-to-SQL) conversion as a main task with query context classification, and passage context classification as auxiliary tasks. We also introduce a method for data augmentation with SQL to natural language task (SQL-to-Text). Additionally, we implemented database-aware query disambiguation for typos and spelling correction by incorporating string and phonetics similarity algorithms. We evaluated the performance of our trained language model (LM) on a test dataset consisting of 1711 natural language queries involving diverse O&G entities, such as fields, wellbore, well logs, markers, etc. Our innovative multi-task approach resulted in a remarkable performance, achieving an exact set match accuracy (EM) of 88.5% on the Text-to-SQL task with impressive F1 scores of 96.3% for query context classification, and 87.7% for passage context classification tasks. We observed multi-task training of LM improved the EM accuracy of the SQL predictions. We also demonstrate the application of our approach to enable contextual natural language search on Open Subsurface Data Universe (OSDUTM) platform. Overall, our approach has potential to transform how end-users search and retrieve data from O&G databases.
Experimental Investigation of Wax Deposition in Model Oils Under the Influence of New Eco-Friendly Wax Inhibitors
Authors: Sharma Rohit , Pattnaik Anuesh , Kar Pratik Raj , Singh Prabh Preet , Tiwari Abhigyan , Deka Barasha 
Published: October 2023

Abstract
This research work focuses on the evaluation of novel and eco-friendly additives as pour point depressants for crude oils to improve the flow properties. Neem oil, rogan kahu, rogan khashkhash, kalonji oil and mahua oil were tested for the pour point studies on a model oil containing two different types of waxes. The study reflected that rogan khaskhas beneficiated crude oil showed highest depression in pour point i.e. up to 6 °C and 10 °C in Type 2 and Type 3 wax embedded base oils respectively, indicating the best additive amongst the five additives tested. Neem oil and rogan kahu also showed encouraging performance in Type 3 wax embedded base oil, where pour point depression was observed as 9 °C by both the additives. Mahua oil and kalonji oil are found to be very less effective in the model oil samples containing the two types of wax. Further beneficiation studies were performed for rogan khaskhas, neem oil and rogan kahu considering their pour point beneficiation which indicated viscosity reduction and wax structure modification for the three additives beneficiated oils. From the results of pour point, viscosity and polarized microscopy experiments, it was observed that rogan khashkhash performance was the best amongst the tested additives as pour point depressant and flow improver.
Energy Efficiency Improvement Technique in Industrial Cooling Tower in Large Petrochemical Complex - Case Study in Sidpec
Authors: Salaheldin M. , Subramanian V. , Salah M. 
Published: October 2023

Abstract
Decarbonization became a crucial topic in all industries especially those working in the energy sector in order to reduce greenhouse gas emissions and mitigate the climate change threat. Energy efficiency has a significant impact on decarbonization, this was clearly announced during COP27, when the Egyptian Ministry of Petroleum and Mineral Resources has announced the energy efficiency strategy in Egyptian Energy Sector during one of the thematic days that was called "Decarbonization Day" and all the companies started to prepare energy efficiency improvement strategy that is aligned with the strategy of the corresponding ministry.
In this paper, an integrated approach for energy efficiency improvement in industrial cooling towers to maximize process and production efficiency, a case study in Sidpec, one of the giant petrochemical plants in Egypt is introduced. The cooling tower optimization is one of the identified opportunities in the company's energy efficiency improvement roadmap which is the backbone of the company decarbonization strategy.
Demand for cooling water has become more pronounced, both in winter and summer because of climate change, and a growing number of warm days which cost the industry millions of dollars annually. In this study, energy consumption issues were resolved in cooling tower in an efficient way with zero capital cost investment.
A 4-step approach for improvement opportunities was applied and was supported by a developed cooling tower simulation model. This approach was successfully implemented and reached the right decision for how a cooling tower optimization and considerable cost reduction can be achieved.
Time-Lapse Inversion of Velocity and Q Factor
Authors: Chen Jianming , Zhao Yansheng 
Published: October 2023

Abstract
Rock properties play a crucial role in influencing the propagation of seismic waves within the Earth, providing valuable insights into various aspects such as the presence of hydrocarbons, porosity, saturation, and permeability variations. Seismic imaging serves two main purposes: mapping the heterogeneity of underground reservoirs and monitoring recovery processes on-site (Nur, 1989). Over time, both production and injection activities alter the response of the reservoir. Time-lapse (or 4-D) seismic monitoring is an essential tool for reservoir management, enabling the tracking of displacement processes. Changes in seismic properties, such as velocity and quality factor Q, can offer quantitative estimates of fluid changes.
Some rock properties related to fluid saturation in the reservoir, such as anelastic absorption quantified by the quality factor Q, can undergo more significant changes than others, such as oil and gas mixed with brine or CO2. To estimate the quality factor Q from seismic reflections, we utilize a tomographic inversion algorithm based on the frequency shift method (Quan and Harris, 1997). This algorithm generates a macro-model for anelastic attenuation. In order to enhance resolution and obtain a micro-model, Poggiagliolmi and Vesnaver (2014) propose a new algorithm that is stable and reliable for computing the instantaneous frequency, which represents the centroid of the instantaneous spectrum. This algorithm improves the stability of calculating the instantaneous frequency and provides a high-resolution image of anelastic absorption. By utilizing this approach, the resulting broadband image of anelastic absorption in depth can offer superior monitoring of reservoir changes compared to conventional methods.
Screening and Evaluation for Asphaltene Dispersants for Hassi Messaoud Field-Algeria: Laboratory Tests
Authors: Djamila Maatallah , Zohra Benaceur Fatima , Ferreira Daniel , Mahmoodaghdam Elham 
Published: October 2023

ABSTRACT
Precipitation and deposition of organic solids such as asphaltene are very common issues for the petroleum industry globally. The deposition of these solids is considered one of the main issues impacting production losses throughout the pore to refinery chain.
Solids induced by asphaltene instability frequently led to well, pipeline and surface equipment plugging, requiring frequent production stops and large -scale and costly operations for removal and to normalize production.
Oil producers develop different prevention-/-mitigation strategies depending on reservoir characteristics; In the case of Hassi Messaoud oil field located in Algeria, asphaltene precipitation and deposition is a recurrent issue from the start of production. The most common solutions adopted are physical removal by scraping the production tubing(slickline) and solvent washes of the near-wellbore area and tubing to maintain or recover production. These options are time consuming and costly and have efficiency limitations, so looking for more efficient solutions has become a priority. Chemical solutions are among the techniques being investigated.
This paper describes the work done evaluating different chemical dispersants as asphaltene treatment for a Hassi Messaoudi oil field. Many commercial asphaltene dispersants were selected and submitted to the laboratory for evaluation to determine their performance and efficiency at reservoir conditions (instead of stock tank oil conditions) to accurately recreate reservoir challenges for proper chemical evaluation.
Work was initiated by performing deposition tests at ambient conditions using various dosages of each chemical, on dead oil samples. Chemicals performance is estimated based on deposition reduction compared to a control with no chemical addition. While these are accepted in industry as quick screening tests, they do not reproduce the complexity of downhole conditions, especially considering the effect of solution gas into asphaltene stability. Therefore, for better understanding of dispersant behavior, laboratory tests were conducted on live oil at specified conditions of temperature, pressure, and shear using Real View®. Prior to that, all dispersants were tested for asphaltene stability at several dosages by a light-scattering technique that combines a solid detection system (SDS) with high-pressure microscopy (HPM).
Innovative Hydrocarbon-Based Additives for Diesel Engines in Oil Field Operations: Mitigating GHG Emissions, Improving Performance, and Advancing Sustainability
Authors: Alrefaai M. M. , Hashmi U. , AlJneibi M. S. , Alhebsi A. A. , Alkatheeri F. O. , A G. De Jesus Geurrero Pena 
Published: October 2023

Abstract
Over the years, in most countries, more strict regulations are being implemented on the emissions resulting from fossil fuels combustion. Among all the conventional fuels used, diesel is considered the one being highly polluting in terms of soot emissions. Therefore many researchers and organizations are continuously looking for technologies to reduce the concentration of particulates produced from diesel burning activities. One of the promising solutions is the hydrocarbon-based additives for Diesel fuel (DF) that can reduce the soot emissions and other harmful pollutants from diesel engine.
Diesel as a fuel and its applications
The fossil fuel mainly diesel and gasoline, are blends of hydrocarbons produced by fractional distillation of crude oil in petroleum refineries [1-3]. Based on the fuel used, the engines were classified into gasoline and diesel engines, which use gasoline and diesel fuels, respectively. Both are internal combustion engines, where chemical energy from the fuel is converted to mechanical energy. The main difference between the two engines is their mechanism of operation. In the gasoline engine, fuel and air are mixed first, and then, the mixture is compressed by pistons before ignition take place by spark generated from the spark plugs. On the other hand, the air is initially compressed in the diesel engine, before the fuel is injected, and then the autoignition of the fuel takes place in hot compressed air [4].
Despite of the fuel type, fossil fuels are generally witnessing an increase in their demand all over the world because of their high energy content and combustion efficiency, consequently, the level of pollutants in the atmosphere is rising as well [5, 6]. DF is mainly used due to their excellent drivability and high durability for many applications such as electric power generation, transportation (including road trucks, military, marine shipping and rail transportation) and off-road uses (e.g., mining, farming and construction) [7]. However, the main concern with the using DF is the pollution caused and its impact on environment [8]. Pollutants are formed as a result of the incomplete combustion in internal engines. Such as soot which known as particulate matter as well, carbon monoxide (CO), nitrogen oxides (NOx), sulfur oxides (SOx) and volatile organic compounds (VOC) [4, 9].
Comprehensive Correlation Analysis of Influencing Factors of Shale Gas Productivity Based on Adaptive Lasso Regression
Authors: Zhao Wentao , Wang Weiyi , Zheng Majia , Luo Di , Xie Mengke , Wu Ya 
Published: October 2023

Abstract
The shale gas demonstration area in the Sichuan Basin is currently at the stage of large-scale development. The main factors controlling the high production of shale gas wells has been a hot research topic in shale gas development.
To obtain the main influencing factors of shale gas productivity, the typical case well area(Z 201)was selected as an example. 12 geological and engineering productivity variables of shale gas well were optimized based on Adaptive Lasso Regression method. In addition, optimization of multiple linear regression model and weight analysis of key parameters affecting productivity were conducted. Finally, the main controlling factors of productivity of shale gas well were identified, and the corresponding productivity prediction equation of shale gas single well was established.
The results showed five independent variables (the drilling length of 1 sublayer, fracture density, thickness, brittleness and maximum principal stress) were optimized based on Adaptive Lasso regression, and the drilling length of 1 sublayer was the main factor controlling the high production of gas wells. In addition, the key to improving the shale gas well productivity were the natural brittle fracture development area and the low stress zone. Based on these study results, a production prediction equation for standardized testing of single well in this area was established, which may guide the drilling operation effectively and improve the production rate of shale gas wells.
The research results have certain guidance and reference significance for shale gas production capacity construction and development. It can provide an important basis for optimizing shale gas favorable areas and evaluating single well productivity.
Bit Wear Prediction Using Adaptive Data Analytics: Dynamic Scenario for Real Time Well Engineering
Authors: Samuel Robello , Liu Zhengchun 
Published: October 2023

Abstract
As the wells become more complex, identifying the locations of critical bit wear becomes increasingly difficult. The analytical models currently used in the drilling industry are not capable of completely realizing the downhole dynamics and thus incapable of bit wear prediction – they either overestimate or are too conservative, while some just require too many parameters, constants and coefficients. There is no acceptable universal mathematical model that describes bit wear and thus this paper proposes not to use a model at all. This study focuses on harnessing the impact of real time downhole dynamics on drilling parameters to predict bit wear using adaptive data analysis techniques.
Real time rate of penetration (ROP) is identified as a parameter that would demonstrate the impact of downhole dynamics with a high degree of functionality. Though other parameters can also be used, ROP is the basic parameter always available. Depending on the parameters available, different parameters can be convoluted together and fed to the algorithm. For instance, ROP and Gamma ray data can be convoluted in an appropriate way to predict bit wear and also predict whether the bit wear is due to formation change or not. The WITSML ROP and drilling data are continuously fed to an adaptive data analytics algorithm which decomposes the incremental ROP data at each depth into its intrinsic mode functions (IMF) using Empirical Mode Decomposition. The trend of the final IMF at each depth is continuously monitored to predict bit wear. For practical purposes of making the whole process fast and straightforward, the energy of the final IMF, referred as IMF energy, is also continuously computed.
In either case, the depth and/or depth interval where the IMF energy becomes negative and/or where the trend of the final IMF plot flips is the depth and/or interval where the bit wear is high and may lead to failure. The proposed workflow has been tested on historical wells and the results are compared with analytical models (B. Rashidi et al 2010 (more conservative) and Z. Liu et al. 2014) used in the industry. The results were also verified with the Daily Drilling reports from the field to confirm the predictions of the algorithm presented. The results predicted were synchronous. The paper presents field examples to predict and estimate the bit wear using actual data. Estimation of rate of penetration (ROP) using the new bit wear model was also carried out using actual field drilling parameters. The calculated ROP profile closely matched with the actual data within reasonable accuracy of less than 5%. Specific procedures are proposed for drilling parameter estimation and effective prediction of ROP and bit life.
Combining Automatic Decline Curve Analysis and Geostatistical Methods to Help Analytical Predictions
Authors: Biver Pierre , Lesage Alexandre , Ong Anthony , Dor Dimitri , Estival Remi 
Published: October 2023

Abstract
Two main methods are used to provide robust production forecasts on existing and new wells. The first option relies on building a geomodel on which reservoir simulations are performed that require long engineering time. The second option is to use the Decline Curve Analysis (DCA) and type curve construction. This method delivers fast and accurate results but relies solely on production data, neglecting the geology, production mechanisms and is often not enough precise to predict future wells’ performance.
In this paper, two workflows combining geostatistics methodology with DCA will be detailed:Workflow 1: A 4 steps’ workflow based on DCA, Distance based Generalized Sensistivity Analysis (DGSA) and type curves. This workflow is relatively time consuming and needs the expertise of a reservoir engineer.Workflow 2: A methodology using DGSA and historical time series interpolation and net gas thickness map
Both workflows have been tested on a tight gas field in Asia to predict, in blind test mode, the cumulative gas produced in June 2022 from all the wells drilled since 01/01/2021 (18 months blind test on 105 wells).
There is no dynamic model available for the field. Wells are produced after fracking and some of them are equipped with velocity string installation.
The prediction of gas production over the last 18 months has been performed with both workflows and lead to similar results.
Overall, the cumulative gas production from all the 105 wells in the last 18 months is accurate at 88% for the more sophisticated workflow 1 and 82% for the simpler workflow 2.
At the well-by-well level the historical vs predicted cumulative production data have a correlation factor of 0.72 for workflow 1 and 0.67 for workflow 2.
Workflow 2 is sufficient to derive quick-look resources of future wells drilled but is less accurate than workflow 1. Both workflows can be run to estimate quick uncertainties.
This work shows the interest of selecting the most appropriate method depending on the accuracy needed to take a decision and the time available. Those two workflows can also be combined to derive uncertainties for stakes associated to future drilling phases evaluations.