bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved Reservoir Rock Porosity Prediction from Advanced Mud Gas Data
Authors: Anifowose F. , Badawood S. 
Published: February 2024

Abstract
In our continued effort to extend the utility of advanced mud gas (AMG) data from the traditional fluid typing to reservoir rock properties prediction, this study investigates the feasibility of predicting the full porosity log for the hydrocarbon-bearing zones of wells ahead of wireline logging and core analysis processes. Our previous incremental results have confirmed the successful prediction of missing porosity logs in an interval within the borehole and in a section of a field.
We established the linear correlation between porosity and Total Gas (TG) to confirm the hypothesis. Leveraging the capability of machine learning (ML) algorithms to recognize hidden patterns in data, we developed artificial neural network (ANN), decision trees (DT), and random forest (RF) models. We collected over 20,000 data points from representative wells in the study area, used 90% for training and optimizing the models, 10% for testing, and five wells for blind validation. A cut-off of 500 ppm was applied on the total gas to remove background gas effects and focus on the hydrocarbon-bearing zones.
Using statistical model performance evaluation metrics comprising correlation coefficient (R2) and mean squared error (MSE), we compared the results of the ANN, DT, and RF models. The RF model consistently outperformed the others based on the training, testing, and validation metrics. Using the original AMG data, the least-performing RF model gave an R2 value of 0.78 for training, 0.76 for validation, and MSE of 0.014 for full-well blind testing. After applying the cut-off, the performance of all the models improved significantly, while the RF model maintained its best performance. With this improvement, the least-performing RF model gave an R2 value of 0.98 for training, 0.89 for validation, and MSE of 0.003 for full-well blind testing. Considering the outcome of our previous studies, these results have further confirmed the robustness of nonlinear solutions based on the ML methodology. It can be concluded that the ML approach for predicting reservoir rock porosity from AMG data acquired in real time is feasible, though with room for improvement. This study also confirms the benefit of focusing on the productive zone by applying a cut-off on the TG.
This study will contribute to the objectives of digital transformation in the petroleum exploration industry by (1) expanding the utility of existing data without extra cost, (2) utilizing real-time data such as the AMG to predict rock properties in real time for better decision, (3) providing more information to optimize reservoir contact while drilling, and (4) providing information to determine reservoir quality at the early stage of well development.
Utilizing Advanced Mud Gas Data to Predict Oil Volume Fraction Using Machine Learning Algorithms
Authors: Anifowose F. , Mezghani M. , Torlov V. 
Published: February 2024

Abstract
Digital transformation is unleashing unprecedented potentials to maximize subsurface data utilization. Advanced mud gas data have been extensively used for reservoir fluid typing. Oil volume fraction (OVF) is traditionally obtained from core analysis and typically computed from wireline logs. Extensive core analysis is expensive and time-consuming. The underlying assumptions of the equations to compute OVF from wireline logs introduce uncertainties. This study proposes a machine learning (ML) approach to predict OVF based on advanced mud gas data to reduce uncertainties and increase accuracy.
There is currently no established empirical correlation between advanced mud gas components and OVF, which creates the opportunity to apply ML algorithms. We applied two models comprising decision tree (DT) and random forest (RF). A multivariate linear regression (MLR) model was implemented as a benchmark. Advanced mud gas data and their corresponding OVF values from 10 wells were used. The gas data comprising 16 light and heavy components are the input features. All the wells were combined to build the dataset used to train the models, leaving one out at each run for blind validation. Out of the results obtained, we present two representative ones in this paper.
We used correlation coefficient (R2) and mean squared error (MSE) as basic metrics to assess the performance of the models. The comparative analysis of the models’ performances showed that the RF model has the highest accuracy for both validation wells to predict OVF using advanced mud gas data. For well A, the RF model has a training R2 value of 0.98 and 0.79 for validation. For well B, the training and validation R2 values for the RF model are 1.0 and 0.84, respectively. The least performing model is the MLR, giving training and validation R2 values of 0.95 and 0.66 for well A and 0.95 and 0.67 for well B, respectively. This indicates that the challenge of predicting OVF using advanced mud gas data is more of nonlinear relationship that is better handled by a ML method rather than a linear model such as the MLR. The MSE for all the models are generally very low. The results also showed that the total normalized hydrocarbon is the most significant feature.
The outcome of this study shows that predicting OVF from mud gas data is feasible using the ML methodology. ML methods are capable of handling the nonlinearity of the data even when the linear correlation is weak. There is also ample room for improving the accuracy of the models by adding more real time data. This study has showed the potential to predict OVF while drilling, ahead of core analysis and wireline data interpretation.
Experimental Study on Plugging Behavior of Temporary Plugging Particles During Dynamic Increase of Fracture Width
Authors: Guo Jianchun , Zhao Feng , Ren Jichuan 
Published: February 2024

Abstract
Temporary Plugging fracturing technology is an effective method to increase fracture complexity in shale oil and gas development. During the temporary plugging process, the gradual increment of net pressure within the fracture leads to a progressive increase of the fracture width, ultimately rendering the temporary plugging zone ineffectual. Therefore, it is crucial to understand the plugging behavior of the temporary plugging zone as the fracture width increases. In this paper, we establish a novel plugging evaluation device capable of dynamically altering the fracture width in real time, while monitoring the plugging pressure within the fracture. The pressure response characteristics of temporary plugging zones with different particle diameters in the process of dynamic fracture width increase were understood. Finally, the influence of injection rate and viscosity on re-plugging of temporary plugging zone is studied. The findings indicate that when the fracture width reaches the critical fracture width, a distinct dominant channel forms between the temporary plugging zone and the fracture wall, causing a significant drop in plugging pressure. The critical fracture width is determined as one-third of the particle diameter, and smaller particle sizes correspond to smaller critical fracture widths. When the fracture width is below the critical fracture width, effective particle rearrangement can be achieved by increasing the injection rate and viscosity to prevent the formation of dominant channels. However, when the fracture width exceeds the critical fracture width, re-plugging the temporary plugging zone through increased injection rate or viscosity is not feasible. In such cases, the dominant channels can only be filled by pumping additional particles. This paper provides the study on the plugging characteristics of the temporary plugging zone under dynamic changes in fracture width for the first time. It proposes the critical fracture width conditions for re-plugging, which serve as a basis for selecting particle size, optimizing injection rate, and viscosity in temporary plugging and diverting fracturing.
Single Phase Retarded Acid System: Innovative Solution for Sustaining Well Productivity
Authors: Sahu Qasim. A. , Kalgonkar Rajendra. A. , Ali Eyad. A. , Alzaid Mustafa. R. 
Published: February 2024

Abstract
Single phase retarded acid systems have been developed as an innovative approach to enhance well productivity. This acid system is designed to maximize contact with the formation by gradually releasing acid over time, which improves well stimulation, flowback initiation, and gas production. The technology involves using minimizing the free water to retard the reaction rate of the acid, which helps to distribute it homogeneously and deeper in the formation. In addition, this acid system can be combined with hydraulic fracturing to further improve production. The use of this innovative solution has shown to be effective in sustaining well productivity and could be a promising alternative to traditional acid systems such as emulsified acid.
The development of the new acid system involves blending a mineral acid with a suitable organic acid, while minimizing the presence of free water in the formulation. This approach helps to control the dissociation of the acid and achieve the desired retardation properties. The single-phase acid system, characterized by its low viscosity, has proven to be highly effective in acid fracturing applications. To assess its potential, the technology was implemented in high temperature and high-pressure conditions, specifically targeting the fracturing of conventional gas wells completed with a plug and perf completion consisting of six stages. The driving force behind field testing was the aim to significantly enhance the fracture half-length compared to the existing standard emulsified acid system. Achieving this objective involved careful control of engineering parameters during the treatment process, as well as optimizing the acid's retardation properties. A comprehensive post-treatment analysis was conducted to evaluate the performance of this acid system, comparing it to previously tested systems. This analysis included pressure transient analysis, post-frac flowback assessments, and friction pressure calculations. Downhole gauges were installed in order to obtain accurate pressure measurements for friction calculations and Pressure Build-up (PBU) analysis. We have recently shared significant progress in the development and implementation of an innovative engineered hybrid acid system, which effectively addresses the limitations associated with the use of emulsified acids. Implementing the new acid system in acid fracturing treatment, we have achieved remarkable success, as indicated by the sustained gas production rate. The treatment outcomes have resulted in a substantial enhancement of the well’ productivity index.
This paper presents a study showcasing the successful utilization of a newly developed retarded acid system for acid fracturing applications in multistage completions. It emphasizes the key design parameters unique to this system and outlines its advantages and limitations compared to traditional retarded acid system.
Optimized Pressure Transient Acquisitions Improve Development Planning of Tight Rich Gas Fields in Sultanate of Oman
Authors: Hegazy A. M. 
Published: February 2024

Abstract
The main goal of this study is to evaluate the effectiveness of Pressure Transient Analysis (PTA) in tight condensate-rich gas fields in Sultanate of Oman. Initially, PTA was used to define the dynamic permeability, total skin factors, and assess impairment due to condensate banking around wellbores. However, after long periods of production, new objectives arose, including assessing impairment due to poor clean-up of fractures in depleted layers. The overall objective is to address PTA different signatures and complications and to use results to improve well and reservoir modeling for better field development planning and the hydrocarbon maturation process.
To achieve the study's objectives, about 35 PTA acquisitions were conducted in four mature clastic gas fields in Oman, which include over 250 fractured wells and have been producing gas and condensate for more than 30 years. The study involved an extensive revision of PTA results to address the main issues of these fields. The researcher estimated most of the actual fracture dynamic parameters and compared them with the corresponding designed parameters. Also, PTA results have been utilized to fine-tune dynamic permeability models, improving the reservoir pressure history matching to give more reliable forecast and development plans.
The study's results showed that proposed individual layer permeability models (controlled by PTA results) were much more reasonable, improving dynamic modeling significantly. Delayed hook-up of fractured wells led to poor fractures clean-out in pressure depleted layers, resulting in weak fracture performance. The actual fracture parameters were much lower than expected before implementation, especially for pressure depleted reservoirs. By controlling the dynamic permeability model of each layer, using PTA results, condensate banking phenomenon has been simulated clearly, considering its negative effect on wells and field production performance. The researcher matched and proved the observed PTA results and prevailing responses (fracturing, condensate-banking) using sophisticated single and Box numerical simulation models. These models were incorporated into full field simulation models, leading to significant improvements in field production forecasts and field development planning.
Consolidation of Carbonate-Based Formation Rocks by Dissolution-Precipitation of Ground Calcium Carbonate
Authors: Alarawi Abeer , AlTammar Murtadha J. , Alrubeh Jasim A. 
Published: February 2024

Abstract
Generating hydraulic fractures with good conductivity is essential for well productivity in unconventional formations. However, hydraulic fractures gradually lose conductivity with hydrocarbon production as fracture surfaces creep closer due to proppant embedment or failure of fracture asperities under increasing effective in-situ stresses and high temperatures. This work proposes adding a widely available, natural-based ground calcium carbonate (GCC) to fracturing treatments in carbonate formations to make fractures mechanically harder and enhance long-term well productivity. The GCC-based solutions with several particle sizes (PS of 5-30 µm) were prepared at 77-300 °F and tested on homogenous carbonate core samples. Several solutions (different PS) were formulated by mixing 20 gm of GCC powder in 100 ml of organic solvent. The core samples were immersed in different solutions for seven days at either 77 or 300 °F, and the samples were thoroughly characterized before and after exposure to the solutions. Rebound hammer tests were also conducted to show the effect of the solutions on the samples’ mechanical properties, such as Young's modulus and compressive strength. The test results show that the physical properties, such as the dry weight of all core samples, increased noticeably after treatment, reflecting the success of the precipitation reaction and the penetration of used GCC into the bulk of the rock samples. Rock treatment with several (PS of 5-30 µm) powder solutions led to a significant enhancement of the samples’ mechanical properties, where Young's modulus (E) and Unconfined Compressive Strength (UCS) were observed to increase by up to 10-25 % compared to untreated rock samples. Rock hardening in fracturing applications is a relatively new concept. Compared to the consolidation additives in the literature, the proposed GCC formula is ubiquitous, widely available, and inexpensive. Lastly, the GCC formula is a by-product of a few oilfield operations, making it a sustainable solution to industrial waste in the oil industry.
Assessing the Heavy Oil Waterflood Efficiency through Integrated Crosswell Testing and Advanced Production Analysis
Authors: Al Harty N. , Rassuli E. , Al Lawati H. , Aslanyan A. , Gulyaev D. N. , Nikonorova A. N. 
Published: February 2024

AbstractThe paper presents a study of a heavy oil mature field in Oman with aggressive water cut growth and slightly exceeding the ultimate recovery as per the initial Master Development Plan expectations. The reserves have been naturally depleted for more than a decade before trying out the waterflood a few years back. The first results of the waterflood were not consistent due to high cross-well interference from one side and possible compartmentalization from another.The key objective of the current study was to assess the on-going waterflood efficiency, cross-well interference, possible production complications and assess possibility of improving further recovery. The key instrument of the cross-well interference analysis was based on multiwell deconvolution of the permanent downhole pressure gauges in response to the historical flow rate variations in offset wells. The water cut diagnostics was based on the large number of well-by-well metrics including recovery micro-modelling baselines, multiphase IPR analysis and multiphase productivity analysis. The mobile reserves’ potential was assessed through material balance, fractional flow analysis and decline curve analysis. Both watercut diagnostics and reserves evaluation have been facilitated by a digital assistant with a fully automated generator of numerous diagnostic metrics which otherwise would take an unrealistically long time to perform such a study.The study has come to the conclusion that all wells are fairly connected but confirmed the deterioration of connectivity between a few wells. The water injectors have confirmed a fair connectivity with all surrounding producers while the aquifer was found to be much weaker than the effect from water injection in these wells. The study suggests that this field still contains commercial volumes of hydrocarbon reserves which can be economically recovered, preferably via horizontal side-tracks from existing wells. It has been recommended to repressurize two main reservoir units independently. The study has spotted a few suspects of thief water production and recommended reservoir-orientated production logging to locate the water source, which was most probably occurring behind the casing. These wells have been recommended as primary candidates for side-tracking.The current study was extensively using a combination of bottomhole pressure deconvolution and advanced watercut diagnostics for heavy oil production to provide a holistic analysis of the remaining reserves. The study also provides the comparison of the results of pressure forecast between multiwell deconvolution technique (MDCV), artificial neural network (ANN) and capacitance-resistivity model (CRM).
CO2 as a Service (CO2aaS) Advisory and Optimization Contractual Models for Critical Production Assets
Authors: Porciani Daniele , Parrella Ilaria , Lauriola Michele 
Published: February 2024

Abstract
Based on the existing knowledge and capabilities of rotating equipment and critical energy systems, industry players are developing and implementing solutions for Emissions Monitoring and Optimization("EM&O") solutions, aiming advanced monitoring and advisory as well as emissions reduction targets within their Environmental, Social and Governance ("ESG") policies (e.g. Oil & Gas Methane Partnership2.0 – "OGMP 2.0" or the United States Environmental Protection Agency GreenHouse Gas Reporting Program – "GHGRP").
New Dimension to Field Wide Cement Evaluation Based on AI & ML for Pro-Active Well Integrity Management in Matured Oil and Gas Fields
Authors: Reddicharla Nagaraju , Vanam Prabhaker Reddy , Voleti Deepak Kumar , Ali Mayada Ali Sultan , Shkeili Ali Abdulla Al 
Published: February 2024

Abstract
Cement evaluation can be performed when well integrity log measurements are combined with the mud and cementing data. However, a truly comprehensive cement evaluation can only be performed when well integrity log measurements are combined with all available well data, including cementing, mud, drilling, geological, and petrophysical data. However, well logging results are complex and can be ambiguous, and decisions associated with significant risks may be taken based on their interpretation based on physics applied in the logging tools. This paper describes a successful application of artificial intelligence and machine learning for cement evaluation which is independent of various logging providers and reservoir conditions.
This in-house solution uses cement bond logs along with other data sets like cement quality parameters, mud parameters, drilling parameters, geological/formation markets information and well type as an input. Historical cement bond log was selection was based on leak off tests. Machine Learning Model was trained separately for each vendor such as Schlumberger, Halliburton, Baker tools with more than 30km of interpreted dataset with high confidence respectively. The ensembled Random Forest and convolution neural network models have been built and analyzed. Cement data quality process has been automated before it is used in model.
Based on predictions, well cement ranking and well integrity recommendations were designed respectively for light weight and heavy weight casing designs based on lift type. All workflows’ steps have been automated to aid process standardization across company. The cement evaluation information is integrated GIS maps for finding aerial and reservoir cross section uncertainties.
The system has resulted a good database for cement evaluation and well integrity that helped to identify potential integrity issues like possible cross flow, communication between reservoir zones with acquirers, possible fluid communication to surface
This approach has replaced manual methods and achieved 95%-time efficiency in decision making cycle. The well retirement process has been improved based on accurate results from this system. This system has helped to challenge the conventional process and been evaluated for ranking the candidates for corrosion monitoring and possible challenges related to well integrity. Workovers have been planned to carry out cement repair jobs in one of matured field for the wells with the age of more than 10 years.
This AI based tool is very cost effective, with minimum human bias, improves consistency and allows proactive well integrity management. This tool can be used to any wells/fields irrespective of their geological setting and geographic locations.
A New Method for the Reuse of Water in the Oilfield
Authors: Choudhary S. , Malekahmadi F. , Moringo N. , sie V. , Liu S. , Li Y. , Price B. , Muhamed M. , Emmanuel I. 
Published: February 2024

Abstract
Increasing environmental, social, and governance values and metrics in the oil and gas industry have resulted in using produced water instead of freshwater for hydraulic fracturing. Produced water has increased total dissolved solids and increased concentrations of multivalent cations compared to freshwater. The altered chemical composition of produced water leads to flocculation and decreased performance of products, specifically emulsion polymer friction reducers traditionally used in freshwater. Flocculation leads to poor regained conductivity, formation damage, and less oil production. Operators may use a higher dosage of emulsion polymer to combat the change in composition, leading to concern over cost, treatment efficacy, and environmental, social, and governance regulations. The use of produced water for hydraulic fracturing necessitates the advancement and development of products.
The compatibility of water and emulsion polymer is necessary to minimize cost and increase treatment efficacy for hydraulic fracturing. Our newly developed emulsion polymer favorably interacts with multivalent cations, resulting in excellent friction reduction in produced waters. In addition, this emulsion polymer avoids flocculation in mid and high TDS brines containing large iron concentrations. This newly developed anionic emulsion polymer is an iron tolerant friction reducer that addresses the growing challenges in utilizing produced water for hydraulic fracturing and fills the gap in the current market.