bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards Net Zero - Better Decision Making for Enterprise Level Green House Gas (GHG) Emission Management and Accounting
Authors: Ratasha Muhammad Aqmal Bin , Ismail M. Syahezat Bin , Aziz Ahmad Sulaiman Abd , Williams Gareth , Rees Emily
Published: February 2024

As a globally recognized energy company, PETRONAS is deeply committed to achieving a balanced approach to energy development and transition, which encompasses energy security, affordability, and sustainability. We remain steadfast in our commitment to optimizing our asset portfolio to lower carbon emission intensity. Our primary focus is on the development of oil and gas resources that incorporate low emissions or emissions abatement technologies, while concurrently pursuing the phased withdrawal of high carbon intensity assets.

Our current emphasis is on reducing Scope 1 emissions, which are emissions directly associated with our operations, as well as Scope 2 emissions, which encompass the energy consumed in our operations. We have revised our carbon emissions accounting method to adhere to international frameworks and sector specific guidance to give us a robust basis for Scope 1 and Scope 2, and a better understanding of Scope 3 greenhouse gas emissions.

Starting in 2023, PETRONAS aims to fully adhere to the Greenhouse Gas Protocol Corporate Accounting and Reporting Standard (2004), Ipieca Climate Change Reporting Framework (2011) and ISO 14064-1: 2018. To provide a total view of emissions across the Group, we will report emissions according to the operational control approach and equity share approach, and include Scope 1, Scope 2 and Scope 3 (prioritizing Category 11 ‘use of sold products’) greenhouse gas emissions. Our strengthened reporting will improve external benchmarking and provide a better correlation between financial and greenhouse gas emissions accounting.
Big Data Analysis Using Unsupervised Machine Learning: K-means Clustering and Isolation Forest Models for Efficient Anomaly Detection and Removal in Complex Lithologies
Authors: Janjua Aneeq Nasir , Abdulraheem Abdulazeez , Tariq Zeeshan
Published: February 2024

Lithology identification holds a pivotal role in the characterization of subsurface formations. In recent years, the advent of big data and the need for more precise lithology identification have spurred the growing adoption of machine learning algorithms. This paper's primary objective is to leverage unsupervised machine learning techniques for the identification and subsequent removal of anomalies inherent in complex datasets. The Isolation Forest model is the cornerstone of our approach for anomaly detection and elimination.

To initiate this process, we employed the K-means algorithm to create clusters, followed by a evaluation using silhouette coefficients. Subsequently, we selected input data for each cluster and conducted exploratory data analysis both prior to and after the removal of outliers. The histograms portraying average anomaly scores for each cluster were thoughtfully presented. For real-time anomaly detection, we harnessed the power of the Isolation Forest model, which facilitated the creation of an Isolation Forest anomalies map by plotting neutron-porosity against bulk density.

Our rigorous data analysis employed various statistical techniques, including data statistics, histograms, and cross plots between neutron-porosity and bulk density. This multifaceted approach successfully eradicated anomalies from the dataset, a fact vividly illustrated by the histograms, where anomalies were discernible through their negative scores. The Isolation Forest map conclusively demonstrated the effective removal of outliers from the dataset, underscoring the model's proficiency in identifying and mitigating these anomalies based on their negative scores.

The Isolation Forest model has thus exhibited remarkable efficacy in the identification and elimination of data anomalies. Its versatility makes it a valuable asset for the detection and removal of outliers, deviations, or noise from datasets, rendering it particularly well-suited for anomaly detection and outlier mitigation in various analytical scenarios. Notably, the combination of the K-means and Isolation Forest algorithms emerges as a potent and advantageous approach, especially when dealing with extensive datasets and conducting comprehensive analyses.
Advanced Machine Learning Framework for Enhanced Lithology Classification and Identification
Authors: Zhang P. , Gao T. , Li R. , Fu J.
Published: February 2024

In this comprehensive study, machine learning (ML) techniques are employed to revolutionize lithology classification within the geosciences, emphasizing the transformative impact of ML on traditional practices. The research encapsulates ML's integration into well-log data analysis, enhancing prediction accuracy and efficiency in lithology identification—a crucial aspect of subsurface exploration.

The methodology adopted includes systematic data preprocessing, feature extraction, and the deployment of advanced ML algorithms such as Support Vector Machines and Random Forest for lithology classification. Models are trained and validated against well-log data from the Teapot Dome Reservoir and the Force 2020 Dataset, with the latter representing a collaborative and competitive environment aimed at advancing ML applications in geoscience.

Results reveal a marked increase in predictive accuracy when incorporating a wider array of logs, as evidenced by Models A1 and A2 for the Teapot Dome Reservoir, and Models B1 and B2 for the Force 2020 Dataset. The research highlights the critical role of ML in achieving high accuracies in lithology prediction, with improved generalization capabilities across different geological settings.

The workflow emphasizes the potential of ML algorithms to enhance well-log interpretation, streamline geological analyses, and reduce the time required for data processing. The study suggests future work focusing on expanding lithology types, normalizing log data, and broadening geographical coverage to further refine ML models for lithology classification. This effort underscores the convergence of ML with geoscience, promising a future where digital technologies create a more interconnected system for subsurface exploration.
Understanding of Geochemical Reactions in Hydrogen-Injected Wells: Cement Integrity for Safe Underground Hydrogen Storage
Authors: Al Dandan Esam , Hossain Md Mofazzal
Published: February 2024

As the world transitions to clean energy sources, Underground Hydrogen Storage (UHS) has emerged as a leading solution for large-scale hydrogen storage. While the depleted oil or gas reservoirs are ideal for UHS, the effect of geochemical reactions among injected hydrogen, wellbore, and cement is not documented. This study aims to assess cement and well integrity by examining the geochemical interaction between API cement and hydrogen near the wellbore under varying temperature and pressure conditions. The numerical simulation was carried out to study the geochemical reaction between hydrogen and API class G/H cement minerals using the PHREEQC version 3 simulator. The dissolution reactions of hydrogen with the initial cement components, namely calcium tetra calcium alumino-ferrite (C4AF), tricalcium aluminate (C3A), tricalcium silicate (C3S), and dicalcium silicate (C2S) were modelled at various pressure and temperature conditions. The simulation assumed continuous cement hydration over an infinite time to assess the long-term effects of hydrogen-cement interactions and its impact on cement integrity near the wellbore. Based on this numerical simulation, we found that at 56.2oC, the formation of calcium silicate hydrate(CSH), portlandite, C3AH6, Mackinawite, magnetite, and hydrotalcite. At 95°C, similar minerals were formed with slightly higher amounts of CSH and slightly less portlandite, while others did not exhibit a noticeable difference. At 119°C, it was observed that a noticeable increase in CSH and a noticeable reduction in portlandite amount. Additionally, the formation of ettringite was observed at elevated temperatures. These findings highlight the temperature- dependent changes in mineral composition near the wellbore, which may have implications for the long-term integrity of the cement matrix in hydrogen-affected environments. Based on comprehensive numerical simulation studies, this paper highlights critical insights for a better understanding of hydrogen-cement interactions in the context of underground hydrogen storage, and its impact on the long-term-integrity of wellbores in hydrogen storage application, essential for enhancing the knowledge base for safe and effective implementation of underground hydrogen storage technologies.
An Intelligent Intermittent Pumping Production Technology Based on Fusion of Photovoltaic Unit and Storage Battery
Authors: Zhang Xishun , Wang Cai , Sun Yizhen , Zhao Ruidong , Shi Junfeng , Yu Yanwen , Ma Gaoqiang , Li Manping , Li Shuangtao , Shen Zhenkun , Xu Jingwen
Published: February 2024

Intermittent pumping for low-producing wells can greatly improve system efficiency and reduce consumption. In order to further make full use of green energy and reduce carbon emission, a set of intelligent photovoltaic microgrid controller is added to the existing intelligent intermittent pumping IOT device. Moreover, an intelligent technology combining intermittent pumping and energy supply fusion of photovoltaic and storage battery driving DC motor is proposed.

The installed power of photovoltaic system and energy storage system is optimized by building a linear integral programming model of photovoltaic-energy storage-load. The optimization method can assist controller in optimizing the intermittent pumping scheme. The photovoltaic-storage intelligent microgrid controller has the function of MPPT, which can be conducted together with the frequency converter control cabinet to replace the function of photovoltaic inverter realizing the direct access of photovoltaic DC voltage to the control cabinet. The technology is based on the electric parameter data of photovoltaic, load, energy storage and other IOT data such as dynamometer card. Combining reservoir, wellbore, equipment and other data and regarding production-energy consumption-operation as the optimization objective function, a real-time optimization method based on self-learning/self-upgrading machine learning model is built. At the same time, the dynamic frequency conversion method is used to optimize the stroke automatically, realizing the adaptive control and flexible operation. The method makes full use of electricity and realizes the integrated control of photovoltaic power consumption and intelligent operation.

The method has been applied into more than 510 wells. Compared with continuous production, the average system efficiency is increased by 4.2 percentage points, the average annual electricity saving is 14.68 million kwh, the annual photovoltaic power generation is 21 million kwh, the carbon emission is reduced by 20,900 tons, the annual oil increase is more than 12,000 tons. Moreover, the manual labor is greatly reduced and the work connotation of oil workers is reshaped which improve development quality and efficiency and promote green and low-carbon development strategy.

This technology effectively solves the problems of real-time optimization of intermittent pumping, real time control, multi-energy complementary optimization forming a green low-cost intelligent intermittent pumping technology system.
The Study of Hydrogen Loss Due to Geochemical Interactions in Depleted Carbonate Hydrocarbon Reservoirs
Authors: Lino Angelo Kennedy Limaluka , Elakneswaran Yogarajah , Hiroyoshi Naoki , Abu-Al-Saud Moataz , Cha Dong Kyu , Ayirala Subhash
Published: February 2024

As hydrogen's role in decarbonizing energy, transportation, and heavy industry expands, it is projected that hydrogen will replace at least 3% of global fossil fuel consumption by the end of this decade. To support this growth, it’s essential to develop larger, safer, and more affordable storage facilities through underground hydrogen storage (UHS). In this study, we investigated the effects of reservoir condition and mineralogy on H2-brine-rock (HBR) system interactions necessary for quantifying hydrogen loss in depleted carbonate hydrocarbon reservoirs and predicting the UHS performance. Static batch simulations of the HBR system thermodynamics were performed in PHREEQC-MATLAB for an extensive range of pressure, temperature, brine, and mineral samples. The results show that, firstly, the influence of formation brine on hydrogen loss is minimal; secondly, temperatures and pressures exceeding 70°C and 23 MPa exhibited hydrogen stability in limestone formations; and thirdly, reservoir pressure exhibited a slightly stronger influence than temperature on hydrogen loss rates under the same mineral composition. Lastly, the result suggests that optimally a pressure of approximately 20 MPa, coupled with temperatures ranging from 90-130°C, represents an equilibrium conducive to stable hydrogen storage within depleted hydrocarbon limestone reservoirs. These findings emphasize the potential viability of UHS in such geological contexts and provide motivation and a basis for investigating the system dynamics.
Aspect-Ratio-Dependent Pore-Size Distribution from MICP Measurement
Authors: Ma Juntao , Liang Lin , AI-Hamad Mohammed Fadhel , Van Steene Marie , Ma Shouxiang Mark , Abdallah Wael
Published: February 2024

Pore size distribution (PSD) is one of the most important properties for characterizing the pore systems of porous media. Typically, a single aspect ratio (α_), defined as the ratio of the short-axis diameter over the long-axis diameter of a pore, is assumed (i.e., set to 1.0) when calculating PSD from mercury injection capillary pressure (MICP) data. This assumption implies that pores can be modeled as cylindrical tubes. In carbonate rocks, the pore network is complex and comprises multiple pore systems with varying values of α_. The objective of this study is to quantify the effect of α_ on PSD derived from MICP measurements.

To achieve that objective, we initially characterize pores with different pore systems (multimodal) with different α_, which is measured from high-resolution digital images or derived from sonic measurements. The α_ distribution from digital images can be grouped into two (bimodal) or three (trimodal) pore systems, with the average α_ for each pore system being calculated. Next, we establish the relationship between capillary pressure (Pc), semi-long axis (a), and α_ for elliptical tubes. This relationship is used to transfer Pc to values of a representing different pore size. We then derive individual PSDs for each pore system by automatically selecting the best-fit combination of pore volume (PV)-based or frequency-based probability density distributions based on MICP data.

We apply the proposed method to two MICP datasets, in which the corresponding α_ measurements from digital images indicate the presence of multimodal pore systems. An experiment-based analytical method and a multi-Gaussian decomposition method were executed for comparison. The results demonstrate that the proposed method provides a better fit to MICP data compared to traditional methods. This improvement arises from its consideration of α_ when converting Pc to pore size. In addition, one challenge with experiment-based analytical methods is that they are not directly based on PSD, and the fitting parameters (such as the Thomeer pore geometrical factor, G) may not be sufficient to describe the complex geometrical characteristics. On the other hand, the multi-Gaussian decomposition method assumes PV-based Gaussian distributions, overlooking frequency-based and other distribution types (e.g., uniform, triangular, beta, gamma, etc.), which the results of the proposed method have been shown to be necessary. By using the proposed method, additional pore-structure parameters, including the fractal dimension describing pore network tortuosity and volume fractions of each pore system, are also obtained during inversion. This yields detailed characterization information on the pore network. By incorporating α_ into the process of converting Pc to pore throat sizes and deriving individual PSDs for each pore system, more details of PSD are revealed, leading to improved reservoir characterization.
Development of Two Robust Data-Driven Models Using Machine Learning and Artificial Neural Network Methods to Predict Wells Fluid Rate in a Challenging Offshore Brown Field
Authors: Eldessouky M. E. , Darwish A. M. , Mohamed I. I. , Abdelhalem T. H. , Khalil A. K.
Published: February 2024

Two robust data driven models based on machine learning (ML) and artificial neural network (ANN) methods were introduced to overcome the shortcomings of physical and virtual well testing in Gulf of Suez offshore fields. The aim of these new models is to use the existing data and create a precise/easily accessible tool that fill the gap in well monitoring and testing system to predict wells fluid rate, improve field optimization and properly allocate oil production.

A comprehensive methodology was applied to build/verify a robust virtual model as following: 1) Analyzing General Energy Equation to select the relevant inputs, 2) Data Collection, 3) Exploratory Data Analysis (EDA), 4) Feature Engineering, 5) Machine Learning Model Selection, 6) Hyper-parameters Fine Tuning, 7) Developing Artificial Neural Network model, 8) Models Deployment.

Exploratory Data Analysis (EDA) and the General Energy Equation were used to select ten main parameters affecting the model’s accuracy. The selected features include wellhead pressure, wellhead temperature, reservoir temperature, reservoir pressure, water gravity, difference between reservoir and bubble point pressure, watercut percent, injection gas, downstream pressure, and tubing type.

Different machine learning models based on linear, support vector machine, decision trees and gradient boosting methods were programmed. The results of these models were compared based on coefficient of determination (R2 score), root mean square error (rmse), mean absolute error (mae), and mean absolute percentage error (mape). XGboost regressor was selected as the best model, then the model hyper parameters were fine-tuned using grid search method. The final model results of test dataset showed R2 score, rmse, mae and mape of 0.9674, 323, 227 and 13.1% respectively.

Furthermore, ANN was created and fine-tuned to select the model architecture. The model was evaluated using the same train and test data where the model showed comparable results to the best ML models. The results of ANN model showed R2 score, rmse, mae and mape of 0.9603, 357, 241 and 13.7%. respectively.
Reliable EACN Determination for Dead and Live Crude in Microemulsion Systems
Authors: Yoga H. F. , Gasimli N. R. , Johns R. T.
Published: February 2024

A successful surfactant flood maximizes oil recovery by achieving ultralow oil/water interfacial tension at the optimum salinity (S*). Optimum salinity, among other parameters, is dependent on the equivalent alkane carbon number (EACN) of the oil pseudocomponent. This paper compares common EACN determination methods used for dead crude at ambient pressure and then proposes a third more consistent and reliable method that simultaneously fits data from both methods. The first method is based on a linear plot of S* and EACN of pure alkanes, where the dead crude EACN is linearly interpolated using the measured lnS* of the crude. The second method determines the crude EACN by iteration until the measured lnS* of the dead crude and all dilution measurements become nearly linear. For live oil, the EACN is based on the common linear EACN mixing rule but corrected for pressure.

The results show that inconsistencies in estimated crude EACN using the common two methods are resolved when regression is made on all data simultaneously and when an unbiased estimate of optimum salinity is made using HLD-NAC theory, where the inverse of three-phase solubility is linear with lnS*. No nonlinear behavior is observed when fit this way and using the simple graphical approach, as has been reported in the literature using the same data. The graphical approach determines the optimal salinity based on the intersection of the linear regressions of inverse oil and water solubility with lnS*. This approach has the advantage that the optimum is unbiased, and its uncertainty is easily estimated. Using a combination of ambient and high-pressure data, we also show that the EACN of the live oil can be estimated using a methane ACN of 1.0, as it should physically be, when the effect of pressure is properly included.
First Horizontal Deployment of Remedial Conformable Sand Control Technology in Colombia - Qualification and Case Study
Authors: Stewart S. , Bermudez J. , Mazo C. , Miri R. , Salimi M. , Nouri A.
Published: February 2024

Remedial sand control is challenging as the solution must pass through restrictions in the upper completion then ideally conform to the inner diameter of the zone requiring sand control. This paper describes the qualification of a multi-layer, open cell matrix polymer (OCMP) solution for the first horizontal deployment in an offshore gas well.

Initial sand retention testing was conducted using a specially designed apparatus that allowed the OCMP to be evaluated at different levels of compression using formation sand suspended in water. The next set of tests were then conducted using equipment capable of replicating field gas flow rate through the optimized OCMP to evaluate sand retention, plugging and erosion. In this case, a sand pack was generated using commercially available sands and clays matched to the formation sand particle size distribution to replicate near wellbore conditions.

It was observed in the initial tests when applying 50% compression, that the OCMP was effective at retaining sand with minimal additional pressure with only 0.026lb/ft2 sand passing throughout the duration of the test. Similar results were seen in the second set of tests where there was zero sand production and no indication of plugging observed neither during the retention tests or after pressure cycling. Erosion of the outer OCMP layer was observed but was filled with sand preventing further erosion of the inner layer. Compatibility with common oilfield fluids including oil, brine, solvents, hydrochloric acid, and hydrofluoric acid was also conducted with no material degradation being experienced. It was concluded that the system was suitable for the application, and it is the intention to deploy the OCMP system on all 3 wells.

This paper introduces, for the first time, an innovative solution capable of being deployed remedially through tight tubing restrictions while providing conformant sand control in a horizontal wellbore without the need for gravel packing making the solution particularly valuable for scenarios where surface space is limited.
Static Adsorption of Novel Synthesized Zwitterionic Surfactant: A Mineralogical Study
Authors: Mohanad Fahmi , Alotaibi Mohammed , Gbadamosi Afeez , Patil Shirish , Kalam Shams , Kamal Muhammad Shahzad , Shakil Syed Muhammad
Published: February 2024

Surfactant flooding process is used to enhance pore scale displacement efficiency in oil reservoir. Usually, minimal retention of surfactant is coveted to achieve an efficient chemical flooding process. This adsorption behavior of a novel synthesized zwitterionic surfactant on reservoir rocks (limestone, sandstone, and dolomite) was investigated in this study. Moreover, the surfactant adsorption behavior on clay mineral was studied. The static adsorption experiment was carried out by mixing 1 g of rock sample with 15 ml of surfactant. The mixture was centrifuged and the supernatant was quantified using HPLC. The result indicates that the surfactant adsorption increases as a function of surfactant concentration. The zwitterionic surfactant displayed lower adsorption on carbonate and dolomite rock samples recording ≈ 1.068 mg/g-rock and 1.389 mg/g-rock, respectively. On the other hand, the hydroxysulfobetaine zwitterionic surfactant displayed higher adsorption on clay mineral. The mechanism of the zwitterionic surfactant adsorption is elucidated. Overall, the outcome of this study extends the frontier of knowledge on zwitterionic surfactant application for chemical enhanced oil recovery.
Accelerating Learning in the Unconventionals Through Integrated Field Experiments
Authors: Benish T. G. , Brown J. S, , Decker K. K. , Habrial J. D. , Liang Y. , Manchanda R. , Meier H. A. , Solomou A. , Srinivasan K. , Vice D. S.
Published: February 2024

This paper provides an overview of unconventional field experiments that ExxonMobil has conducted in recent years to measure the dimensions of fractures generated in the subsurface. These learnings are combined with production analysis to support and inform development decisions to avoid overcapitalization from suboptimal spacing, stacking, or stimulation choices.

Specific field experiments exemplify the importance of experimental design and instrumentation, as well as associated deployment challenges. Highlighted are the benefits of dedicated monitor wells equipped with fiber optics and pressure gauge arrays to interrogate the three-dimensional fracture network. While challenging to execute, this approach provides definitive measurements of the extent and variability of fracture dimensions and depleted rock volume, and requires the integration of downhole measurements, interpretation, and tool automation.

Vertical monitor wells are effective in determining the overall height of the created fractures as well as the drainage boundaries of individual benches. Slant monitor wells extend these measurements of fracture density and depletion at varying distances from a stimulated well to enable mapping of the fracture drainage network. Systematic variation of the completion design along the treatment well adjacent to a slant monitor well can be implemented to directly compare the efficacy of treatment design options in the same well.

To handle the large amounts of collected data from field experiments, automation of diagnostics workflows and integration of the data in a single tool become imperative. Pressure measurements are integrated with fiber optic measurements to provide a holistic interpretation of the extent and heterogeneity of the stimulated rock volume.

Due to their complexity and cost, instrumented slant monitor wells are rarely executed. A summary of multiple field trials are provided here where well designs, deployment, data collection, and integrated analysis were used to aid development planning and optimization.
Understanding Pore Scale Emulsification During Surfactant Injection: A Visualization of Emulsion Formation Using Microchips and Core Flooding Visualizing Cell
Authors: Alyousef M. , Alade O. S. , Al Hamad J. , Al-Abdrabalnabi R. , Al Ramadhan A.
Published: February 2024

Utilizing a surfactant solution to create an emulsion in a rock's pore space has been proven to enhance heavy oil production. By obstructing some of the pores in the rock, fingering is reduced, and micro-displacement efficiency is improved, ultimately increasing oil recovery. This technique is also valuable in reducing the interfacial tension between oil and water by inducing phase dispersion and altering the physical characteristics of the oil in the reservoir. While previous research has concentrated on increasing oil recovery through in-situ emulsion, there has not been much exploration into the pore-scale level of emulsion formation and its impact on recovery performance. Our study aims to explore the effectiveness of polyvinyl alcohol (PVA) surfactant as an emulsifying agent for medium to heavy crude oil, leading to the formation of water-oil emulsions. Our methodology involves injecting formation water into the microchip using a pump to achieve initial water saturation and adding oil to attain irreducible water saturation. Lastly, we will introduce the PVA surfactant to the chip and use image processing software "ImageJ" to analyze images throughout the process to gather valuable insights about its performance compared to alkaline injection. During the microfluidics experiment, it was observed that introducing PVA surfactant to the system resulted in the recovery of 27.8% of the Initial Oil in Place (IOIP). In contrast, alkaline injection yielded a lower recovery factor of 20.5%. This is attributed to the superior emulsion formation capabilities of PVA injection, leading to increased droplets, which caused blockage and prevented micro water channel formation, ultimately increasing the recovery factor. Notably, when the experiment was conducted under reservoir conditions, the recovery factor of the PVA surfactant decreased to 17.5%. However, it still demonstrated the ability to form emulsions. PVA surfactant was found to be a favorable option as it did not cause any damage or alteration to the rock sample, as confirmed by Scanning Electron Microscopy (SEM). The SEM analysis showed no change to the rock sample before and after conditioning with the surfactant for one week. Therefore, based on these findings, PVA surfactant is deemed a viable and practical option along with other surfactant types, such as Anionic surfactants, for enhanced oil recovery. The results suggest that PVA surfactant injection presents superior emulsion formation capabilities compared to alkaline injection. It is recommended that further research be conducted to explore further potential applications of PVA surfactants in EOR.
What Low Frequency Distributed Acoustic Sensing Revealed of The Hydraulic Fracturing in Tight Oil Reservoir: Ordos Case Study
Authors: Qi Yin , Zhang Tongwu , Chen Qiang , Ma Bing , Bai Jie , Ye Kai , Tang Jie
Published: February 2024

Real-time low-frequency distributed acoustic sensing (LF-DAS) data was acquired using a cemented, permanent fiber optic cable for the first time during hydraulic fracturing of the offset horizontal well in Ordos tight oil reservoir. Based on high-quality LF-DAS data, cross-well communication (CWC) analysis was done stage by stage while completing the treatment well using plug-and-perforating (PnP). Clear frac-hit responses were observed during each stage's treatment, and most fractures hit the offset fiber well very early, which was far beyond the operator's knowledge and triggered a rethinking of the current completion and stimulation plan. Diagnostic tools like sealed wellbore pressure monitoring (SWPM) and microseismic mapping validated the frac hit events. Reactivation responses of the fractures created in early stages were easy to identify and aid in evaluating stage isolation effectiveness. After this case, further work could be considered from qualitative results to quantitative outputs.
Establishing Best Practices for Well Abandonment in CCS
Authors: Enriquez H. Costeno , Dick H. , Salter R. , Lee S.
Published: February 2024

In CO2 storage sites, wells located within the predicted area of review, and that penetrate the confining zone, may become a leakage pathway out of the injection zone if not properly abandoned. Prior to injection, the abandonment focus is on existing (legacy) wells. After injection has concluded, the focus will expand to include storage development wells, e.g., injector and monitoring wells. Unlike oil and gas developments, where formation fluids are produced, Carbon Capture and Sequestration (CCS) projects inject CO2 into downhole formations, creating a plume that must be contained within the target reservoirs for periods of time that are longer than oil and gas wells’ life span. These differences make it necessary to develop CCS-specific well abandonment practices.

The authors have analyzed industry-accepted well abandonment standards, abandonment recommendations and requirements from regulators for CCS projects, and several case studies and laboratory experiments on well integrity to develop an understanding of the challenges and probable solutions for CCS well abandonment. Then, a recommended practice for well abandonment in CO2 storage sites that addresses isolation of the confining and reservoir zones, the effects of a CO2-rich environment on well materials, and corrective action for legacy wells will be presented.

Most of the existing regulations and standards on CCS establish expectations and objectives for well abandonment without providing detailed guidelines that can be consistently applied across projects. Without consistent rules for CCS well abandonment, project teams will conduct design or planning exercises that will have varying outcomes as they adapt conventional well abandonment guidelines to CO2 storage site requirements. This may result in under- or over-designing the well abandonment; which could translate into compromised well integrity, impact on project value; and an ever-changing technical standpoint. The aim of a recommended practice is to establish simple high-level principles with which to approach well abandonment in CO2 storage sites to facilitate the work of project teams and to have an acceptable level of consistency on abandonment plans.

This paper will demonstrate a comprehensive abandonment strategy for CCS and legacy wells. The strategy addresses CCS-related containment concerns and local applicable abandonment regulations for all other sources of inflow not related to CCS. It discusses how the ability of a confining zone to stop the flux of CO2 through it, that depends on its permeability, thickness and capillary pressure; is relevant for establishing CCS abandonment principles.
Advancing Efficiency in Drilling: Developing a Robust AI Model for Real-Time Bit Wear Estimation
Authors: Luu Trieu Phat , Huang Xu , Zhan Guodong David , Aljohar Abdulwahab , Qahtani Yazeed , Furlong Ted , Bomidi John
Published: February 2024

The main objective of this study is to develop a robust AI model to estimate the level of bit wear during the real-time field deployments. We aim to enhance drilling efficiency by accurately predicting bit wear, considering limited offset training data with minimal subsurface information. The proposed model has practical implications, enabling informed decisions, optimized maintenance schedules, and reduced downtime in drilling operations.

We collected drilling parameters (e.g., RPM, ROP), and subsurface data (e.g., gamma) from seven offset wells to train the AI model. We handled missing data and outliers through interpolation. The data were then standardized to ensure consistent scales, and segmented into windows. An LSTM-VAE model was designed and trained in an unsupervised manner. To predict bit wear, the trained AI model was first employed to project each window data into a latent space, similarity scores across runs were then computed, and an XGBoost model was trained to predict bit wear based on relative positions of input data in the latent space.

In the field real-time model study, ten bits from five different vendors were examined to assess the performance of the AI model. To test the model's robustness, only Gamma ray logging was utilized for formation interpretation. The AI model successfully predicted the dull status for six out of eight qualified bit evaluations. Furthermore, the model accurately predicted the dull grade for bits from different vendors, indicating its agnosticism to specific bit types. These results validate the robustness and transferability of the developed AI bit wear model, particularly in challenging drilling applications.

The novelty of this paper lies in the development of an AI model for real-time bit wear estimation, considering limited inputs and offset training data. The use of an LSTM-VAE model, coupled with XGBoost for estimation, presents a unique approach to address the challenges of accurate bit wear prediction. Additionally, this study introduces the implementation of similarity scores across runs to further simplify the latent space, enhancing the model's robustness.
Application of Deep Neural Network (DNN) in Reservoir Simulation for Hydraulic Fracturing and Production of Unconventional Wells
Authors: Li R. , Fu J. , Gao T. , Zhang P.
Published: February 2024

This study focuses on developing a Deep Neural Network (DNN)-based reservoir simulator aimed at improving the efficiency and precision of hydraulic fracturing and production forecasting. Given the complex and nonlinear nature of reservoir behavior, our goal is to implement an automated, scalable solution to effectively navigate these challenges. The proposed DNN simulator integrates diverse data sources, enhancing a comprehensive understanding of reservoir dynamics and supporting real-time decision-making and operational optimization.

Reservoir simulation, a key numerical method for predicting fluid behavior in porous media like oil and gas reservoirs, is vital for crafting and refining production strategies in the oil and gas industry. A swift and accurate simulation is essential for effective field development.

The study particularly addresses unconventional wells—horizontal wells fractured to facilitate oil and gas flow—where challenges persist in areas like fracture geometry characterization, fluid dynamics between the matrix and fractures, and pressure variation prediction during field development. The scarcity and diversity of data, encompassing both the volume and variety from sources like geology, geophysics, lab tests, and engineering, add complexity to simulating these wells.

We explore the use of Deep Neural Networks, a machine learning algorithm capable of deciphering intricate data relationships, in this context. Recent advancements in machine learning have opened avenues for employing DNNs in reservoir simulation, which is a supplement to traditional time-intensive mathematical algorithms. We constructed and simulated both three-dimensional conceptual models and unconventional field models using this approach. The results validate the deep learning method's accuracy and speed, showcasing its supplementary to conventional numerical reservoir simulators.
Production Forecasting and History Matching of Hydraulically Fractured Reservoirs Using a Pressure Depletion Volume (PDV) Method
Authors: Tian Yakai , Weijermars Ruud
Published: February 2024

The production rate and cumulative production of hydraulically fractured shale wells can be estimated using the pressure depletion volume (PDV) method. The Gaussian Pressure Transient (GPT) is used to compute the pressure depletion in the drainage region of single or multiple hydraulically fractured wells, and the pressure depletion is then translated to production performance. This new approach does not involve Darcy’s Law, and therefore provides an independent method to evaluate well performance. The pressure depletion in reservoir volume between hydraulic fractures is computed by integrating the normalized GPT for the fractured reservoir region, accounting for each individual fracture. Also included is the pressure drop in the nearby reservoir region from pressure changes initiated via the fracture tips. The total pressure depletion of the drained reservoir, can then be computed for each moment in time as an instantaneous analytical solution. The cumulative production is computed using from the comprehensive compressibility coefficient of the drained reservoir space. The daily production rate can then be computed from the time derivative of the cumulative production at any moment in time. To validate the PVD method, the production rate forecasts were history-matched to (1) real production data from the Eagle Ford shale formation, and separately, to (2) synthetic, noise-free CMG-IMEX production data. Both data sets could be satisfactorily matched. The PVD model can also quantify the relative contribution to production from the fracture tips and fracture box region, as well as determine how their relative importance switches over time. The PDV-method proposed in this paper is based on the GPT model, and can predict both the pressure depletion and production performance over the anticipated field life prior to drilling, which is helpful for optimizing completion designs and maximizing economic benefits.
ERD Record in Shallow Weakly Consolidated Formation with Remarkable Efficiency
Authors: Xie Tao , Hou Xinxin , Lin Hai , Zhang Lei , Li Tianyang , Li Zhiheng , Wang Wen , Wang Guiping , Chen Kejin
Published: February 2024

Extended reach wells are drilled to develop the remaining oil in shallow formation just around 600~1400m in China Bohai oilfield. The formation there is terrestrial sediment and heterogeneity is strong, moreover, only water-based drilling fluid can be used as Bohai Bay is an internal bay with weak self-cleaning ability. For these reasons, precise well trajectory control, wellbore cleaning, casing running and cementing are challenges in this region. Facing the problems above, a set of safe and efficient drilling technology for shallow extended reach wells has been developed. Firstly, well profile is optimized referring previous drilling experience, secondly, well trajectory is redesigned using software and directional drilling tools with high buildup rate are selected for precise well trajectory control; thirdly, drilling fluid properties are continuously improved to maintain wellbore cleaning and smooth; Lastly, innovative floating while rotating casing running method and cementing technology are employed to reduce the friction in casing running and avoid leakage during cementing job. The design build rate is about 4 degree/100ft, while the natural inclination reduction rate of formation is serious using classic directional drilling tools that fail to meet this requirement. Thus, uniform wall thickness Positive Drive Motor with 1.5 bend angle and Rotary Steering System of push-the-bit type are selected in surface and second spud drilling to ensure adequate build rate with assistance of software which can track and modify well trajectory while drilling. Water based environmental protection drilling fluid with low viscosity and high shear force are developed to remove cuttings bed and reduce ECD. Meanwhile, the inhibition and plugging of drilling fluid are enhanced to avoid hydration expansion of mudstone. The tripping and back reaming efficiency have been improved by 20%. The drag and torque of casing running in different ways are calculated, innovative floating while rotating casing running method is proposed comprehensively considering the rig capacity and the torsional strength of the casing. Low density cementing slurry system and slurry column structure are optimized to reduce ECD while cementing and cementing quality is excellent. From 2019 to 2021, these technologies have been successfully applied in 7 extended reach wells, with an average drilling efficiency of 97.1%, and the field applications show that the ROP has been improved by 40% while well construction time has been reduced by 28.4% compared with previous operation. Moreover, the horizontal displacement to vertical depth ratio of W1H well is as high as 3.23, breaking the record of Bohai oilfield.
Exploring CCUS Well Construction Using Fit-For-Purpose Cement Designs
Authors: Shine J. M. , Qasmi U. , Elhancha A.
Published: February 2024

As the industry moves towards well construction for underground storage projects, there could be uncertainties on the requirements for long term zonal isolation. The scope of a storage project should help with the well construction FEED though the applicability of utilizing fit-for-purpose cement design could be overlooked as well as overdesigned based upon the notion of "corrosion risks" for well integrity. The cementing design approach should look holistically at the corrosive risks and isolation requirements as opposed to a single solution approach or wort case design independent of the well utility. The manuscript discusses the approach to designing fit-for-purpose cement designs to achieve long term zonal isolation for a future CCUS project.
A Case Study Appling Multi-Well Saturation Height Modeling Combined with Pore Geometry Advanced Petrophysical Rock Types for Integrated Reservoir Characterization and Field Development Studies
Authors: Gunter G. W. , Sahar M. Y.
Published: February 2024

Abstract and Overview

The objective of this extended abstract is to highlight how integrated studies can be enhanced and reduce uncertainty by applying these new methods including multi-well saturation height functions, advanced petrophysical rock typing methods, advanced core-log integration, machine learning & neural network algorithms. We include a generic reservoir case study showing the improved workflow.

A major challenge in most studies is how to propagate fluid saturation in a 3D model. Using the new Multi-Well Saturation Height Function method allows for improved cored-log integration of Petrophysical Rock Types (PRT) using Open Hole logs. To tackle this issue, an integrated workflow has been deployed which included the following steps (Gunter et.al. 2018, 2020-2022).

Lithofacies, pore geometry, pore types, core data and fluid contacts must be understood. Core-log integration to calibrate core and rock properties from deterministic to probalistic methods should be used and results compared. Petrophysical Rock Types definition and validation at the cored wells are extended to the non-core wells using Statistical/Probabilistic methods. A multi-well Saturation Height Modeling (SHM) is implemented based on Petrophysical Rock Types and matching fluid saturations from well logs. This multi-well approach allows a better estimation of Free Water level, improved understanding of reservoir compartmentalization and reduced uncertainty over traditional single well saturation height models.

The case study shows how this new workflow and application provides an efficient distribution of fluid saturations based on capillary theory, fluid contacts, petrophysical rock types, and pore geometry. Successful 3D models must have an excellent geological representation of the reservoir system including thin section information, mineral composition calibration, pore geometry, capillary properties, and flow capacity of reservoir units. Core log integration must be validated to identify cutoffs and then define petrophysical rock types and permeability equations.

The above enables, from statistical standpoint, successful selection of the inputs logs that would discriminates between the defined rock types through a Linear Discriminant Analysis, which is a critical point in the performance rock types propagation in the non-cored intervals/ wells.

Utilization of multi-well saturation height model improves results, the 3D distribution of the fluids and identifies fluid contacts with less uncertainty, than single well based methods.