bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental Investigation of Non-Chemical CO2 Microbubbles Eor Performance in Low-Permeability Reservoirs
Authors: Jia Haowei , Yu Haiyang , Tang Huiting , Shi Jianchao , Tang Engao , Wang Yang 
Published: February 2024

Abstract
As a novel, economic, and environmentally friendly enhanced oil recovery (EOR) and carbon sequestration technology, non-chemical CO2 microbubble (MB) has potential applications in low permeability reservoirs. At present, there are only few studies available focusing on non-chemical MB EOR in low permeability reservoirs. Previous studies mainly focused on its storage efficiency in saline aquifers, and more research is needed to fully understand the EOR mechanism in low permeability. In this paper, the EOR performance and its mechanisms of non-chemical CO2 MB in low permeability reservoirs are experimentally investigated. For comparations, a series of CO2-based method were also included, such as CO2 injection, water altering gas and conventional foam.
The results demonstrate that CO2 MB has competitive EOR performance to conventional foam injection. The increment oil recovery of microbubbles on 2.23×10−3 μm2 and 9.46×10−3 μm2 rock samples are 11.74% and 19.59% original oil in place (OOIP), respectively. In parallel coreflood experiment, the MB increased oil recovery by 10.73% and 17.92% of OOIP on core samples with the permeability of 9.43×10−3μm2 and 2.25×10−3μm2, respectively. The CT imaging shows that the core sample has lots of residual oil zones due to microheterogeneity. After MB flooding, the average residual oil saturation of the core sample is reduced from 42.15% to 33.5% and the horizontal and vertical residual oil zones are eliminated. The results of this study comprehensively evaluated the feasibility and EOR performance of non-chemical CO2 MB in low permeability reservoirs. Overall, the results suggest that non-chemical CO2 MB is an efficient EOR method which has better displacement efficiency and conformance control ability than other CO2-based non-chemical EOR method (CO2 injection and WAG).
Mitigating Nonproductive Time: A Novel Algorithm for Dsl Fault Detection
Authors: Couto-Silva C. M. , Shetty S. , Olid-Gonzalez A. , Wallez G. , Chatelet V. , Kohar A. 
Published: February 2024

Abstract
Digital slickline (DSL) has been introduced to improve the efficiency of intervention operations in both onshore and offshore wells. DSL cables provide a real-time two-way-telemetry path between the acquisition system at the surface and the downhole sensors. Most nonproductive time (NPT) in DSL operations stems from telemetry issues due to cable faults despite the system's robustness. To address this, we developed a data-driven framework for identifying potential cable damage and its approximate location using the DSL logging telemetry data, including communication signals, pressure, and depth. We tested our method on 992 real-case downhole jobs across almost 30 countries. To validate our method, we compared the method predictions for 60 jobs with labeled potential faults (i.e., cable damage), reaching an accuracy of 98% when considering whether the job has a fault. Thus, our framework enhances cable management, reducing NPT and associated costs.
Biomethane Value Chain Enhancement: Development of a Digital Tool for Preliminary Techno-economic Evaluations and Sustainability Assessment
Authors: Abbate F. , Bosetti A. , Guidolin M. , Pinna R. S. , Dovico L. , Franco R. , Lacquaniti M. , Menegon S. , Zampato M. 
Published: February 2024

Abstract
"2030 Agenda for Sustainable Development" document has been assumed by the international community as the main guideline to be followed in order to ensure the undertaking of the path towards sustainability and the achievement of the sustainability target.
As an international energy company, Eni is heavily committed with the sustainable development goals (SDGs) and is sponsoring various initiatives to achieve them.
Biogas production through anaerobic digestion as well as biomethane represent one of the main goals to be achieved in the bioenergy field. Energy transitions give biogas and biomethane the opportunity to gain a firmer foothold in global energy consumption. Biogas and biomethane start from a low base, but are the fastest-growing forms of bioenergy in both the STEPS and the SDS. Their combined market share in total modern bioenergy demand grows from 5% today to 12% by 2040 in STEPS and to 20% in SDS [21]
In this context, Eni (R&D, EniProgetti, DIT) developed an innovative and integrated tool which deals with chemical, engineering and digitalization topics related to feedstock-to-biomethane chain, called hereinafter "the Tool".
The Tool is a software suite able to perform technical/economical assessments of plants for transformation of feedstock into biomethane through anaerobic digestion (AD) and biogas upgrading processes. Its target users are represented by expert developers in charge of devising the most convenient plant configuration, considering plant requirements, efficiency, capital and operating costs.
Estimation of Gas Initial In-Place Utilizing Well Surface Data Via Supervised Machine Learning Approach
Authors: Ejaz Muhtashim , Mehmood Saad , Azeem Abdul , Hussain Sadam 
Published: February 2024

Abstract
Machine learning techniques are being implemented across many industries, including oil and gas, disrupting traditional workflows. The objective of this paper is to provide an effective, robust and an alternate data-driven methodology to estimate gas initial in-place utilizing well surface data. A machine leaning assisted workflow has been established to transform variable rate & wellhead pressure history into constant rate & pressure response, which is subsequently translated into flowing P/Z for in-place calculation.
The study concept is based on developing a relationship of flowing wellhead pressure (FWHP) against independent variables via supervised machine learning approach. Multiple linear regression algorithm (MLR) was adopted which models the linear relationship between a single dependent continuous variable and multiple predictor variables. In this study flowing wellhead pressure (FWHP) was modelled against gas rate and cumulative production. FWHP vs gas rate training was performed to establish FWHP response with changing gas rate reflecting well productivity, while FWHP vs cumulative production training was performed to determine FWHP reduction with depletion.
The methodology involved typical machine learning pipeline steps including data collection and cleaning, feature engineering, model training & tuning and model deployment. Feature engineering was the most critical step where representative variables were identified and manipulated to improve model prediction accuracy. The data set of each well was split into test/train set (~ 40/60%) and model accuracy was determined via R-squared technique. The best-fit model was then used to generate FWHP profile against constant gas rate, which was then transformed into flowing P/Z to calculate gas initial in-place GIIP.
The above procedure was performed on several gas producers. It was identified that FWHP was predicted with reasonable degree of accuracy when trained against feature set consistent for all wells, derived from gas rate and cumulative production. The initial gas in-place subsequently estimated was in line with conventional techniques in all cases validating the reliability of this approach. It was also identified that ~ 20-25% production data was adequate to develop robust ML model providing reliable GIIP estimates.
Conventional hydrocarbon initial-in-place estimation techniques require acquisition of downhole data resulting in frequent well shut-in and/or utilization of commercially available applications. The above explained machine learning approach provides equally reasonable in-place estimation utilizing merely surface data, reducing the requirement of extensive downhole acquisition.
Intelligent Solution for Auto-Detecting Lithology Scheme While Drilling by Machine Learning
Authors: Gamal Hany , Elkatatny Salaheldin , Abdulaziz Abdulaziz M. 
Published: February 2024

Abstract
Predicting the lithology type of drilled formation is a critical task in the drilling operations as it significantly affects the drilling program and the drilling operation's total cost. Hence, it is highly recommended to detect the lithology variation while drilling to be able to optimize the drilling parameters based on the penetrated lithology type. Currently, the lithology changes are estimated from the geological data and logs which are considered as operations limitations and drawbacks of this approach as cost and lag time. The current study proposes an intelligent machine learning solution for auto-detecting the formation tops and lithology types of drilled formations while drilling in real-time utilizing drilling surface data.
Machine learning techniques are technically employed for developing real-time prediction models for the drilled rock lithology from the surface rig sensor data as weight on bit, drillstring speed, hook load, mud pumping rate, torque, pumping pressure and rate, and rate of penetration as model input data to predict the drilled lithology class. Different ML techniques Decision Tree, K Neighbors Classifier, and Bagging Classifier were tested through the methodology to assess the computational power for classifying and auto-detecting the drilled lithology while drilling by feeding real-time drilling data to the models. The data set represents a complex lithology of five different drilled formations (dolomite formation, anhydrite, dolomitic limestone composition, limestone, and shale formation), while the dataset is utilized for training and testing purposes. The methodology approach presents a comprehensive layout for data collection, preprocessing, data statistics and analytics, feature engineering, model development and parameters optimization, and prediction performance evaluation.
The results showed a high prediction performance for the models for training and testing with an overall accuracy higher than 98 through detecting complex lithology schemes. Predicting the drilled formation's tops and lithology while drilling in real-time will provide a technical guide for optimizing the drilling parameters for better drilling performance and optimized mechanical-specific energy.
Field Trial Test to Minimize Flaring in Deepwater Gas Field
Authors: Kiertkul Suphawat , Yusoff Mohd Faizal 
Published: February 2024

Abstract
Block H consists of deepwater gas wells tie-back to FLNG via 1,830 meters of dual risers. During LNG plant upset, a minimum feed gas is required not only to supply fuel gas, but also to sustain the riser stability to prevent slugging and surge due to nature of dynamic gas flow in deepwater risers. The objective is to perform field trial tests to stretch the turndown limit to minimize flaring to environmental impact during process shutdown.
An OLGA model was revisited to run several cases below the design limit to review the well pressures, subsea pressures, liquid hold-up fraction profiles, total liquid hold-up profile in each subsea component as well as topside liquid arrival rates. A work instruction was practically developed for the field trial test to compromise the lower limit of the riser turndown against flow stability from the wellbore, XMT, flowlines, risers, all the way to Inlet receiving facilities at topside. A mitigation plan was prepared to prevent slugging flow and avoid operation interruption in overall system at topside facilities and subsea production system.
The results from the field trial test indicated a lower limit of the riser turn down could be as low as 50 MMSCFD for a period. A step reduction of 5MMSCFD feed gas from the turndown design limit of 70MMSCFD was performed to monitor dynamic flow behavior in the entire system to adjusted real-time process parameters. At the flow lowered than 60MMSCFD, an increase in liquid holdup at subsea and liquid levels at topside separators were observed, but manageable thru the level control valves in the separator integrated system and process control loops. However, at a flow rate lower than 50MMSCFD, an unstable flow in the one flowline was detected as well as instability at the wellbore. This is because each wellbore requires a minimum flow rate for deliquification. Besides, the multiphase flow regime in the riser section subsequently started to change from annular to churn flow and eventually became slugging flowing to the topside as liquid level at slug catchers and separators increased. The decision was to shut down one Rotan well and minimize the three wells from the field to ensure enough energy could sweep out liquid at the risers and flowlines while minimizing flaring during this period.
Optimizing flare requires a comprehensive approach considering various points of process parameters to avoid operation interruption. The compromise solution to sustain overall stability is to bring down the riser turndown rate as low as reasonably practical and leading to the flare reduction at downstream of Gas Metering system up to 20MMSCFD during the process shutdown. This reduced flare gas emission from the field to atmosphere around 3,033 tons of CO2 equivalent in 2022.
Development of a Mechanistic and Data-Driven Model for Multiphase Flow Leak Detection in Pipeline
Authors: Barooah Abinash , Khan Muhammad Saad , Rahman Mohammad Azizur , Hassan Rashid , Hassan Ibrahim , Sleiti Ahmad K. , Gomari Sina Rezaei , Hamilton Matthew 
Published: February 2024

Abstract
Prompt and reliable detection of pipeline leaks is vital for human safety, the economy, the environment, and corporate reputation. However, a simple mechanistic model for accurately predicting leak characteristics in different flow regimes is lacking. To fill this gap, a novel methodology was used to develop a multiphase flow leak detection model using only inlet and outlet parameters.
The gas-liquid two-phase leak mass flow rate, location, and size are computed through iterative processes in the upstream and downstream sections of the leak. Data sets were generated for a wide range of geometric (3 – 5 inch pipe diameter, 2000 – 10000 feet pipe length, 500 – 1500 feet leak location, 0.2 – 3 inch leak opening diameter), hydrodynamic (Newtonian, air, CO2, N2) and operating conditions (0.3 – 0.628 outlet liquid fraction). These data sets were utilized to develop contour plots and a data-driven model using statistical analysis based solely on the inlet and outlet parameters.
The results indicate that a change in total mass flow rate and pressure in the inlet and the outlet section of the leak can be a good indicator for determining the location and size of the leak. The effect of different pressure constraints, pipe length, pipe diameter, two-phase fluid rheology, leak diameter, leak location, outlet liquid volume fraction, and flowing liquid hold-up on leak size, pressure, and flow rate is analyzed. Decreasing the liquid fraction in the outlet section of the leak leads to a slight increase (6% average) in the inlet mass flow rate and a significant decrease (50% average) in the outlet mass flow rate for fixed pressure constraints, resulting in an increased leak flow rate, pressure, and density. Similarly, longer pipe lengths, bigger pipe diameters, heavier gas phase, and lower liquid fraction at the outlet have higher leak pressure for the same leak locations due to higher leak flow rate. Furthermore, contour plots revealed that identifying a leak near the pipe inlet is easier, although determining its size remains challenging. On the other hand, detecting a leak near the pipe outlet is more difficult, but assessing its size is comparatively easier. The developed data-driven showed good agreement with different literature data sets with a MAPE of less than 20%.
The mechanistic model's key advantage lies in its reliance on fundamental equations and physics, making it applicable to various operating conditions for field applications. Moreover, the data-driven model is straightforward and accurate, eliminating the need for complex simulations. This study has the potential to assist industries in determining leak location, size, and pressure using only the inlet and outlet parameters, without requiring multiple sensors along pipelines.
Use of Cellulosic Esters Derived from Coffee Ground Wastes as Sorbent Material for the Purification of Produced Water from Kuwaiti Oil Extraction Operations
Authors: Salem F. Al , Thiemann T. , Polouse V. , Alshamsi H. , Alaryani M. , Alshamsi M. , Selem B. , Youssef M. , Hasan S. 
Published: February 2024

Abstract
It has been noted that worldwide at least 6 million tons of spent coffee grounds (SCGs) are generated annually. This is why a novel means of purifying produced water (PW) from oil production processes in Kuwait using biomass derived from spent coffee grounds (SCGs) as a final step is under investigation. Cellulosic material gained from CGs is functionalized by esterification utilizing acetyl (C2) octanoyl (C8) or palmitoyl chloride (C16). The esterification is carried out to further augment the hydrophobicity of the torrefied, extracted SCG-cellulose to allow for the adsorption of oil and other lipophilic contaminants from the PW. Sorption properties of the thus derived cellulosic esters in combination with activated carbons are reported.
Synergistic Scale Inhibitor Blends for Severe Carbonate Scaling Systems
Authors: Johnston C. J. , Jordan M. M. , Vikshaland K. , Johnston M. 
Published: February 2024

Abstract
Control of inorganic sulphate and carbonate scales with polymer and phosphonate based scale inhibitors is well established within the oilfield chemical industry. The potential for synergistic interactions with blends of these chemical types to give reduced treatment rates, lower chemical discharge volumes and potentially lower treatment cost specifically for carbonate scale control and better energy efficiency, is less well understood.
This paper presents the selection and field trial application of such a synergistic blend designed to control severe scaling within produced fluid heaters on a North Sea platform, however this blend has the potential to be applied in other environments such as CO2 WAG production systems, heaters and heat exchangers, for example.
Inhibitor performance tests were carried out to evaluate inhibition of a range of single component inhibitors before blends of these chemicals were evaluated to try to reduce the inhibitor concentration required to control both calcium carbonate (saturation ratio, SR 550, mass 1100mg/l) and barium sulphate (SR 55, mass 450mg/l) scale formation.
For this challenging carbonate scale environment at high temperature (105°C), it was observed that a blend of a polymer (carboxylic acid functionalized polymer) and the currently applied phosphonate was more effective than either of the components by themselves, suggesting synergistic interaction.
Results from the initial field trial of the synergistic blend are presented with monitoring methods outlined to confirm that the formulation is as effective as the laboratory evaluated tests suggested.
The initial trial started at the incumbent product injection rate for 1 week with differential pressure across the topside heaters, fluid flow rate and fluid heating performance being trended to confirm scale control prior to a 20% reduction in treatment rate being applied for 1 week with a further reduction of 40% of the incumbent being applied for another 7 days prior to the full dose rate of the incumbent chemical being reinstated to allow full review of the trial results.
The methods outlined in this study demonstrate the potential to reduce chemical treatment rate, cost and environmental impact by evaluating the synergistic interaction of the current range of commercially available scale inhibitors and therefore eliminating the high registration costs and time associated with bringing a new molecule to market. This study also offers the potential for synergistic blends to be utilized to inhibit severe carbonate scales in challenging environments including extreme calcite scaling environments such as CO2 WAG production, high temperature topside heaters, alkaline surfactant polymer flooding and heat exchangers.
Performance Aspects of Water Quality in Disposal Wells
Authors: Alfakher Basil , Smith Kern , Alhamad Luai , Ulloa Joel 
Published: February 2024

Abstract
Water quality is one of the most significant variables that influence the injectivity in matrix disposal wells. Permeability reduction through water/rock interaction or pore throat blocking are two common mechanisms of injectivity loss. This paper investigates the relative contribution of fluid/water compatibilities, water/rock interactions and all of the other water borne contaminants on formation damage.
A series of systematic experiments were conducted to determine the most significant parameters contribution to potential formation damage mechanisms, their contribution to the cumulative damage, and possible treatment designs to restore core permeability. The proposed analytical workflow included a comprehensive characterization of potential injection water (both liquid and solids) utilizing advanced analytical techniques to characterize water quality in terms of salinity and solid organic and inorganic contamination such as ICP and XRD. In addition, coreflooding experiments were performed to quantify the impact of oil content, dissolved and suspended solids, and particle sizes on the injectivity loss as a function of time core plugs. Treatments to remove or bypass the damage to regain permeability were also investigated.
Experimental results uncovered the water parameters that contribute to formation damage such as water salinity, oil-in-water content, and suspended solids. The average particle size of the suspended particles was in the order of 10 microns. Coreflooding results indicate that the water severely damaged cores up to 42%, with permeability to air below 30 md. The water composition, along with the high total suspended solids played an important role in the damage observed in the lab experiments. Total suspended solids and scaling tendency of the water accounted for over 60% of the damage, with oil content for the remainder. Based on the analysis of the relative contribution of the various damage mechanisms, for low permeability (25 – 50 mD), the following water quality requirements would be recommended to best manage the loss of injectivity: oil in water content less than 100 ppm, total suspended solids less than 50 mg/L with an average particle solids size less than 5 microns.
In this paper, a systematic analytical workflow is investigated that provides methodology of screening water quality for injection purposes. The methodology can be adapted to different water sources for enhanced injectivity and disposal.