bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing Different Formation Evaluation Methods to Distinguish Low Resistivity Pay in Carbonate Reservoirs
Authors: Alshuwaikhat Ali , Mostefai Ilies , Al-Nasser Mamdouh 
Published: February 2024

Abstract
Low resistivity pay is one of the main challenges that exist in carbonate reservoirs with complex pore structure. When hydrocarbon charging occurs, it displaces water in larger pores with lower entry pressure, leading to a complex fluid distribution. As resistivity logging is based on how easily electrical current tends to flow through formation, a rock made up of micro pores filled with water will exhibit a misleading low resistivity reading that can highly influence fluid saturation and formation evaluation when using simple logging tools such as resistivity.
The objective of this paper is to provide an assessment of two different formation evaluation methods: resistivity logs and capture cross-section (formation sigma) across low resistivity pay intervals. The workflow includes a thorough evaluation of water salinity across the targeted field. Then, candidates were selected to compare results between the different methods to evaluate hydrocarbon and water saturations, as well as bound fluid volume. These results were then benchmarked and validated against formation testing and sampling (FTS) and surface production data to identify level of accuracy and limitation of each technique.
Results of this study show that sigma-based saturation can be considered as reliable across LRP intervals in simple rock mineralogy carbonate reservoirs, and it is recommended to compliment triple-combo logs to flag such challenging zones. The computed hydrocarbon and water saturation is based on set sigma values for each fluid type and lithology, which are inputted into the volumetric equation. On the other hand, resistivity log tends to underestimate hydrocarbon saturation using conventional Archie's equation. NMR logs and core analysis provide an additional knowledge of porosity distribution and existence of micro-porosity in the formation. Fresh water injection, as well as any variation in formation salinity would highly impact both saturation computation methods.
Combining sigma and triple combo logs would lead to increasing engineers’ confidence in precise horizontal well placement in hydrocarbon-bearing low resistivity intervals, as well as accurate identification of fluid contacts in vertical wells. Additionally, this could lead to a significant optimization in unnecessary sampling jobs and completion designs and its associated costs.
Application of Representative Stochastic Models to Guide Development Decision Making in Large Unconventional Projects with Variable Fluid Composition
Authors: Narayanan Mahesh , Macphee Michael , Weight Robert , Alghaithi Ghaith 
Published: February 2024

Abstract
Commercial development of large green field unconventional projects with concurrent late-stage appraisal and initial development drilling is fraught with challenges. While the resource may be laterally extensive, extracting the resource efficiently is an engineering challenge that involves decision making at multiple stages to sequence and stack segments so as to maximize value and minimize risk. In large unconventional developments, allocating capital for appraisal and development sequencing benefits from a structured stochastic approach addressing risks and uncertainties to remove subjectivity. This paper illustrates the application of stochastic methods to guide management decision making during initial project development.
Large unconventional plays show spatial variability in fluid composition and productivity. A good appraisal program ensures sufficient wells are drilled, completed and tested to understand this variability. Learnings from such programs combined with learnings from analogue wells aid in categorizing PVT and well performance variability to identify priority areas of focus. Based on this analysis, representative models are determined for fluid windows incorporating a range of expected well deliverability. Using a structured stochastic approach, these representative models leverage a bespoke economic workflow integrating multiple uncertainties, constraints, costs and risks through a stochastic method. The workflow shown in this paper is based on synthetic data.
Based on a range of outcomes, decision making metrics such as probability of commerciality, expected monetary value and peak funding exposure are calculated integrating value of information criteria. The key output from this integrated workflow enables prioritization of focus areas for appraisal and development including stacking and sequencing of resource segments to ensure effective capital allocation and supporting optimum development value.
This paper highlights the benefit of applying a stochastic economic method to guide management decision making at the early stages of project development. The aim is to support management with an unbiased scientific approach which considers multiple uncertainties. This method helps prioritize appraisal and development options with multifaceted criteria for decision making and capital allocation.
Machine Learning Application for Hydraulic Fracturing Optimization in a China Tight Gas Field
Authors: Yu Tianjun , Li Ming , Wang Taiji , Mullen Kevin , Zhang Jie , Audrey Beryl , Yang Hai Hua , Kartoatmodjo Gayatri 
Published: February 2024

Abstract
The normal approach to fracturing optimization in any tight formation involves comparing the production from multiple wells after one or two controlled changes to the design parameters. This approach might not adequately consider the importance of each factor nor disclose hidden relationships between them. The new approach presented in this paper involves applying machine learning to optimize fracturing designs by understanding factors affecting production and by predicting production using a data set of historic wells’ geological, fracturing and production parameters.
This approach proposed here, when applied to our development asset, increased the prior frac optimization parameters by multiple factors, including geological properties, fracturing parameters, and production data, which will also be applied to predict gas production. The data from 110 wells containing Shan2 and Benxi reservoirs were prepared, trained, and analyzed. Then, 14 trial algorithms were ranked by R2 scores and the best one was used to perform a sensitivity analysis between the factors and production. This result can be used to optimize design parameters and achieve the most economic design.
Randomly chosen training and testing data sets were used to compare algorithms. Based on R2 scores, a gradient-boosted tree algorithm applicable for both reservoirs was determined to be the best. This algorithm showed that effective thickness and pumping rate have had the greatest impact to gas production from the Shan2 reservoir production, but gas saturation and proppant concentration are the most important for wells producing from the Benxi reservoir. These factors then become the main adjustable parameters in the fracturing design.
These results were then applied to guide hydraulic fracturing designs of six new wells. The final designs were based on optimizing both production and operating costs. Well testing showed that optimized designs achieved an overall 27% increase in absolute open flow compared with standard designs. Optimized designs incur marginally higher costs, this is offset by a 33% increase in 2 years of cumulative production, which represents an overall economic improvement in this development scenario.
This principal benefit of the machine learning approach is to create a robust decision making for adjusting hydraulic fracturing designs based on production and economics. In addition, prediction accuracy improves over time with the addition of new wells and longer production history. Finally, this novel approach is proven to maximize gas well productivity and to optimize materials and logistics.
Evaluation of Real-Time and Post-Drilling Petrophysical Data Acquisition Impact on Wellbore Stability and Mechanical Rock Integrity
Authors: Qatari Ammar 
Published: February 2024

Abstract
The objective of this paper is to examine filtrate and mud solids invasion effect on wellbore stability and rock mechanical integrity. A stable wellbore depends on the mechanical and chemical interaction between the wellbore fluid and the walls of the wellbore. Excessive wellbore pressure can cause lost circulation and low pressure of the wellbore can cause blowout or collapse. Multiple factors affect mechanical integrity of the rock including the time at which the acquisition of rock mechanical data was taken in the subsurface. The impact of invasion is measured by the exploitation of real-time and post-drilling petrophysical data. A thorough investigation of invasion and its effect on rock mechanical properties is performed to establish a full understanding of the association between time dependency and rock integrity. A mechanical earth model (MEM) is built utilizing petrophysical data acquired in both real-time and post-drilling. Mechanical properties are then cross checked with core measurements to examine the accuracy of the results. The effect of invasion is then highlighted showcasing the time dependency effect on both wellbore stability and rock mechanical integrity.
Leveraging real-time and post-drilling petrophysical data across abrasive sandstone formation is key to investigate invasion effects. The effects were witnessed in the readings of resistivity. Separation of deep, medium, and shallow resistivities were observed highlighting the invasion effect due to the time passed after the drilling process and before logging the section. When it comes to invasion effects on strength of the rock, an (MEM) was run on a well with both LWD and wireline acoustic data. Fracture point was analyzed for the effects of invasion. A data comparison is highlighted showcasing the effect of time on the integrity of the rock. Capillary force and osmotic pressure effects are examined and cross checked with the logged data and wellbore stability impact. This paper examines the direct impact of invasion on the mechanical properties of the rock along with wellbore stability. Complex formations can be problematic in lithology during the drilling operation where it might be capable of creating issues such as stuck pipe. The geomechanics of borehole stress has a direct impact on the hazards and problems encountered during drilling operation which causes inefficiency in terms of time and cost spent operationally. The full understanding of invasion effect is a potential solution to such issues.
Diesel and Emissions Tracking in Real Time: Moving Towards Sustainable Fracturing
Authors: Khan Abdul Muqtadir , Narang Rishika , Kurniadi Stevanus Dwi , Ryan Abraham , Joshi Vaibhav 
Published: February 2024

Abstract
Every organization in the oil and gas industry is currently focused on optimizing hydrocarbon production using innovative technologies and infrastructure to meet environmental stewardship goals. This study details a methodology to enable real-time tracking of diesel consumption during the hydraulic fracturing treatment. Diesel volumetrics can then be easily upscaled to CO2 equivalent emissions with the correct knowledge of the emission factor.
A digital dashboard is built that can stream the data with cloud or on-prem services. In the asset performance monitoring, all engine parameters such as rotations per minute (RPM) of each piece of equipment are recorded; fuel efficiency factors convert these data to the equivalent diesel totalizer. An idle time will be also recorded when the RPM is recorded outside the application's range and will be used to optimize the idle run. An effective methodology is presented to calibrate the fuel efficiency factors utilizing actual diesel measurements. The entire dataset recorded is uploaded into the operator's cloud structure. The results of on-the-fly calculations and comparisons of the performance of the assets are displayed on a dashboard with the actual diesel. Our focus in this work is on five components of emissions in hydraulic fracturing: high-pressure pumps, blender, hydration unit, and transportation of water and proppant. However, for other purposes, the dashboard and digital architecture can be expanded across the entire process from supply chain to post-treatment activities.
This methodology will enable the operators to conduct route planning and minimize diesel consumption across all activities. It provides the decision maker with real-time information on the instantaneous emissions footprint, unlike the current measurement that relies on postmortem calculation. It also provides the analysis of operations, logistics, methods, and material efficiency impact on emissions.
A Successful Case Study from China's First Self-Operated Project Using an Openhole Shunted Screen Gravel-Pack Completion
Authors: Wei Min , Cun Xihong , Liu Xiaoli , Zhao Geng , Liu Yaolan , Xu Bin , Ren Xiaoqiang , Zhang Yong 
Published: February 2024

Abstract
Two openhole multilateral wells (Well 1 with two laterals and Well 2 with one lateral) from China's first self-operated offshore project were completed with openhole shunted screen gravel-pack completions, which is the first application of shunted tube screens in this type of lateral well in China. The challenges of these two wells, analysis of the design of both tools and fluid, and key operational points have been documented. To date, production has doubled by use of the method in the two wells.
The project design includes the openhole shunted screen gravel-pack completion string and high-performance slurries to overcome installation challenges and provide high gravel-packing efficiency. To achieve efficiency, multiple segments and parties worked together, including the drilling, completion tools, pumping fluids, and pumping equipment teams. Each well was installed with shunted screen gravel-pack equipment, and more than 60 m3 of fluids and 17 m3 of gravel were displaced.
The project was successfully executed in Q3 2022. This is the first time this solution has been deployed in China to solve a series challenges including uncertainties regarding leakage and wellbore stability around the laterals and potential unstable shale streaks in the long openhole section.
Generative Artificial Intelligence for Geomodeling
Authors: Misra Siddharth , Chen Jungang , Churilova Polina , Falola Yusuf 
Published: February 2024

Abstract
Subsurface earth models, also known as geomodels, are essential for characterizing and developing complex subsurface systems. Traditional geomodel generation methods, such as multiple-point statistics, can be time-consuming and computationally expensive. Generative Artificial Intelligence (AI) offers a promising alternative, with the potential to generate high-quality geomodels more quickly and efficiently. This paper proposes a deep-learning-based generative AI for geomodeling that comprises two deep learning models: a hierarchical vector-quantized variational autoencoder (VQ-VAE-2) and a PixelSNAIL autoregressive model. The VQ-VAE-2 learns to massively compress geomodels into a low-dimensional, discrete latent representation. The PixelSNAIL then learns the prior distribution of the latent codes. To generate a geomodel, the PixelSNAIL samples from the prior distribution of latent codes and the decoder of the VQ-VAE-2 converts the sampled latent code to a newly constructed geomodel. The PixelSNAIL can be used for unconditional or conditional geomodel generation. In unconditional generation, the generative workflow generates an ensemble of geomodels without any constraint. In conditional geomodel generation, the generative workflow generates an ensemble of geomodels similar to a user-defined source geomodel. This facilitates the control and manipulation of the generated geomodels. To improve the generation of fluvial channels in the geomodels, we use perceptual loss instead of the traditional mean absolute error loss in the VQ-VAE-2 model. At a specific compression ratio, the proposed Generative AI method generates multi-attribute geomodels of higher quality than single-attribute geomodels.
Rapid Rock Properties Estimation from Micro-CT Images by Deep Leaning
Authors: Alsamadony Khalid , Alabdrabulrasul Ali , Mahmoud Mohamed 
Published: February 2024

Abstract
Digital rock physics (DRP) can compute several rock properties such as porosity, tortuosity, formation factor, permeability, thermal conductivity, and elasticity. To calculate these properties, the DRP workflow usually includes micro-CT imaging, processing the images (filtration and segmentation), and simulation of physical processes. These steps can be time-consuming and prone to human error.
In this study, three image datasets of a sandstone sample were used. The first dataset includes the original micro-CT images without any processing. The second dataset contains micro-CT images filtered by the non-local means filter to reduce the noise. The third dataset was created by segmenting the filtered micro-CT images. From these segmented images, porosity and tortuosity were calculated. Tortuosity was computed using pore network modeling (PNM). Three deep convolutional neural networks (DCNNs) were developed to predict tortuosity and porosity from the image datasets. The accuracy of the predictions was assessed by the average absolute percentage error (AAPE) and correlation coefficient (R).
The predictions from the three image datasets are comparable. R and AAPE of the predicted porosity by DCNNs are about 0.99 and (1%-1.8%). R and AAPE of the predicted tortuosity by DCNNs are about 0.93 and (5.9%-6.2%). The results show that simple rock properties (porosity) are more accurately predicted by DCNN than advanced rock properties (tortuosity). The predictions from the micro-CT images (without any processing) are almost as accurate as the predictions from the filtered and segmented images. This could be due to the use of deeper DCNN. Therefore, DCNN can quickly predict some rock properties from unprocessed micro-CT images without significant loss of accuracy.
This paper shows that DCNN has the potential to eliminate the need for image processing and simulation in the DRP workflow, which may allow semi-automation of the DRP workflow. Additionally, micro-CT scanner manufacturers may be able to provide end-to-end solutions. In this case, raw data (unreconstructed projections) could be used with micro-CT images to enhance the accuracy of the predicted rock properties.
Improving Underlying Image by Resolving Velocity Anomaly and Compensating for Seismic Absorption Using Q Pre-Stack Depth Migration: A Case Study of 3d Land Sukowati Indonesia
Authors: Subhan M. , Kusuma A. S. , Razi M. , Sipahutar H. , Amperianto A. , Agustina A. S. , Biramandita D. 
Published: February 2024

Abstract
For discovering new opportunities in the northwest of the Sukowati Field reservoir, that limited by the low-resolution images of the subsurface, especially for the target reservoir. The existence of changes in lateral velocity variations, especially in reverse-fault areas and carbonate reservoirs, necessitates the need for an accurate velocity model with validation of the well data, Q Tomography to compensate for the absorption energy, and the migration algorithm that can image the complex subsurface structure.
The final velocity model used the Tilted Transverse Isotropic (TTI) anisotropy, by adding the theta and phi (azimuth) models to the migration algorithm, which increased the focus of events, especially on faults, and reflector dips. The PSDM results are also tied to the well marker, indicating validation of the accuracy of the VMB results. The QPSDM application is used for amplitude attenuation compensation in areas with weak amplitudes, QPSDM can also broaden the frequency spectrum. During the migration process, Beam Migration can handle multi-arrival ray traces, making it suitable for application in complex areas.
All of the velocity model building processes, resulting in reasonable lateral and vertical velocities controlled by well data, and validated by understanding the geology along the survey area. The final velocity and anisotropy models follow the geological structure well and match the sonic and checkshot information contained in the well data. Beam Migration results produce a better image of carbonate body and layers under the carbonate, which become the target reservoir. This is reinforced by the results of last two drilled wells, the potential of the clastic reservoir has been proven, opening up development opportunities to the west to pursue structural closure of the Transgressive Wings Carbonate.
The limited quality of legacy seismic data to see the distribution of reservoirs and limitations in the interpretation of geological structures in the Sukowati Field can be overcome by QPSDM results that produce a more accurate map of the reservoir depth structure, higher resolution of seismic data, minimize uncertainty, and open up opportunities to seek new prospects in the northwest region.
Economic Analysis of Gas to Liquid Technology as the Solution for Stranded Gas Commercialization in Indonesia
Authors: Damayanti L. H. , Burhanudin M. , Sukmato H. , Wijaya A. , Darmawanto D. 
Published: February 2024

Abstract
Indonesian energy demand continuously increases in line with economic development and population growth. The average demand growth in the past ten years has been around 6% annually. The demand is supplied primarily by oil (47%). As domestic oil resource is no longer sufficient to fulfill domestic demand, Indonesia's energy policy moved away from oil by promoting the utilization of more abundantly available energy resources such as natural gas. Indonesia has natural gas reserves of 34.65 TCF in 2023. However, such reserves still have not been developed due to economic and physical reasons, called stranded gas. The development of new technology that can commercialize stranded gas is expected to fulfill the growing energy needs.
The common problem of stranded gas is when it is remote from the gas market, making the construction of a pipeline prohibitively expensive. Alternative ways of converting gas into denser energy and transporting the energy using the non-pipeline mode to the customers need to be developed. Gas-to-liquid (GTL) technology is one of the technologies currently being developed because of its ability to process natural gas to produce synthetic liquid fuels that are similar to petroleum-derived products, even with better quality so that they can reduce the portion of petroleum; both as a source of energy and as other industrial raw materials. Using Gas to Liquid technology, Natural Gas can be turned into Fischer-Tropsch synthetic fuel /FT Diesel. The FT Diesel specification matches the quality of petroleum-based diesel fuel. FT diesel is the most promising fuel as the quality requirement of diesel fuel is strengthened in the future, and it is urged to introduce FT diesel at an early date to make the most of its character of being environmentally clean while attempting to secure its demand base and creation of demand for the product in the future.
This paper presents the feasibility of GTL technology implementation to monetize stranded gas, which is assessed using the wellhead netback price of gas as a feasibility indicator. The netback price is calculated using the price of fuels to be substituted by diesel (in this case, Solar) as references. The basic question of this study is the minimum gas price at the wellhead that satisfies the GTL project investor's economic criteria when the gas is converted to GTL, and the GTL is to substitute solar consumption. The minimum acceptable rate of return (MARR) is set at 16% as an economic criteria evaluation.
In addition to feasibility analysis, this study also evaluates Indonesian regulatory viability for GTL development, including its product and benefits to the nation, as well as its implementation risk.