bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ahead 9 Months of First Hydrocarbon with Minimum Re-Engineering and CSU for the Field in Malaysia
Authors: Shatiry M. Sahir A. , Ismail M. Zukhairi , Ros M. Ikhranizam M. , Beserdin Nur Hafizah , Ismail A. Hisham , Nor Hasnul Hakim M. , Mahamud M. Adwim , Salehuddin M. Yazid 
Published: September 2024

Abstract
Field X is under development and combines greenfield and brownfield projects. The project consisted of the development of 2 new wellhead platforms named Platform AA and Platform BB, where the new Platform was a tie-in to the existing live platforms A and B under Field X. Based on the project's overall execution strategy, the construction and installation of the new Platform AA and BB was executed first, then continued with the brownfield scope which related to modification of the existing facilities to receive the hydrocarbon from new wellhead platform. Based on the project schedule, the production from Platform BB will be introduced first in 2022, whereas Platform AA will be introduced in 2024. Thus, all the engineering designs for the new tie-in platform were per the detailed engineering design. In the 2022 offshore campaign, the completion of the drilling campaign at the new wellhead platform BB then continued with the brownfield scope on the existing Platform B, where new equipment, such as a new venting system and well test system, upgraded the control and safety system to electronic system, new chemical injection skid for the pipeline and upgraded the existing wells headers and control system. The year 2022 has brought the project team success with the earlier-than-planned hydrocarbon from new wells at Platform BB through the existing process facility at Platform B.
With this in mind, the Project Management Team (PMT) has looked at the prospect of flowing the wells at Platform AA in 2023 rather than 2024, as was initially planned. Through the exhaustive alternative explored on the possibilities and total dedication from the team, the team came up with the solution for the early flow with the "Interim Engineering and Start-up for Early flow" strategy for Platform AA. With a minimum re-engineering and modification of the existing facility at Platform A, the selected concept provides the process route for the hydrocarbon from Platform AA to flow into the processing facility at Platform A before entering the gas and liquid export trunkline. After several hurdles to complete all phases from engineering, procurement, construction, and commissioning (EPCC), the team successfully flowed 5 wells from the new Platform AA in November 2023.
Real-Time Artificial Intelligence-Enhanced Machine Learning Technique for Accurate Drilling Parameter Prediction and Optimization
Authors: Elahifar Behzad 
Published: September 2024

Abstract
This study aims to utilize machine learning algorithms to predict drilling parameters, specifically Weight on Bit (WOB), Flowrate, and Revolutions Per Minute (RPM), for an undrilled well within a geological formation. This prediction aims to maximize the Rate of Penetration (ROP) in the well to be drilled and tune the AI model in real-time (using a Wired Drill Pipe) with the downhole data to update input drilling parameters automatically.
A dataset was utilized from four existing wells within the same geological formation, containing historical drilling parameters (WOB, Flowrate, RPM, and ROP). Various machine learning algorithms were used, including Linear Regression, Support Vector Machine (SVM) Regression, Gradient Boosting Regressors, K-Nearest Neighbors (KNN), Ridge and Lasso Regression, Gaussian Process Regression, Decision Trees, Random Forest, Bayesian Regression, and AutoML (TPOT), to build predictive models. The models were rigorously evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) to identify the best-performing and most accurate model.
The study resulted in the development of an optimized machine-learning model capable of predicting drilling parameters for the undrilled well. This predictive model is instrumental in achieving maximum ROP. As drilling progresses for the next well, real-time data is incorporated to continually fine-tune and update the machine learning model, ensuring adaptability to real-case conditions. The integration of wired drill pipe and the collection of real-time downhole parameters further enhance the model's accuracy, revolutionizing drilling operations by combining data-driven insights with real-time feedback to achieve unprecedented drilling efficiency.
This paper introduces a novel and innovative approach in the petroleum industry, leveraging machine learning for predictive drilling parameter optimization. By incorporating real-time data and feedback, the study enhances the accuracy of drilling parameter predictions and aims to automate input drilling parameters with high precision. This additive contribution advances the state of knowledge in well drilling operations, significantly improving efficiency and drilling performance, and reducing non-productive time of the operation.
Damage Classification of Oil & Gas Equipment Components Using Siamese Networks
Authors: Sirothia Aaradhy , Nair Gaurav , Pandya Rohan 
Published: September 2024

Abstract
Visual inspections are non-destructive testing methods that involve the assessment of a material, component, or product to determine its current condition and maintenance schedule. This is done visually, either unaided or with the assistance of a tool. It serves as the initial check for both new and in-service equipment. The process involves scrutinizing the quality of surfaces to uncover any superficial flaws that may have occurred during production or might develop during operation. Although visual inspection is an integral part of any integrity asset management strategy, current visual inspection workflows are marked by experience-driven rules, based on which one can assign damage severity levels to any type of equipment or component. The evolution of the latest technologies for capturing images (e.g., drones, 360° cameras) has reduced the requirements of physical inspections in the field. However, the number of images captured by these devices in a single iteration is huge (e.g., 100,000+). Manually inspecting these images is a persistent challenge and requires a significant amount of human effort, time, cost, and consistency across the entire process. Hence, the need for an automated visual inspection workflow is paramount. The paper introduces a Siamese network-based damage classification application that enables faster execution of the visual inspection workflow. Siamese networks work by learning the features associated with a class based on differences and similarities between given triplets of images. The methodology has an added advantage over conventional classification approaches in terms of the data required to train the model for different equipment. The embeddings for a pre-defined set of images are generated and stored. Comparison of any unseen image embedding is performed against this set of predefined embeddings. Based on the similarity of the unseen embedding with the existing set, a damage class is assigned to the given image. By reducing the amount of data needed, the methodology enables quick extension of the existing workflow to previously unseen equipment.
Reuse of Existing CO2 Pipeline and Pipeline Rights-Of-Way for Large-Scale CCS Deployments
Authors: Ma Z. , Chen B. , Pawar R. J. 
Published: September 2024

AbstractCarbon capture and storage (CCS) is an important technology to mitigate climate change and the proper design of CCS infrastructure is essential for large-scale CCS deployments. However, constructing new CCS transport infrastructure is expensive, requiring substantial investments and extensive negotiations with various landowners. Given that many pipeline infrastructures, such as pipelines and pipeline rights-of-way (ROWs), are available, it is anticipated that converting these existing infrastructures for transportation would be beneficial for CCS deployments. Therefore, this work is focused on reusing existing infrastructures to reduce the overall costs and accelerating CCS deployments. To reuse existing CO2 pipelines and ROWs, we first obtained and analyzed various data on CO2 pipelines and ROWs. Next, we integrated the existing pipeline route data into a generated candidate pipeline network for optimization. For the existing pipeline ROWs, we generated an updated cost weight surface map based on various Geographic Information System (GIS) information, which was then utilized to the subsequent pipeline network optimization. We tested the newly developed computational capabilities on CO2 pipeline infrastructure design projects in the Intermountain West (I-West) region of the United States, where multiple existing CO2 pipelines and ROWs of pipelines (e.g., natural gas and crude oil pipelines) are available. The results show that the reutilization of existing infrastructure impacts the optimal CCS pipeline network, including pipeline routes and overall transport costs. Specifically, if the existing CO2 pipelines meet the transport requirements, they can potentially be used without constructing new ones. On the other hand, if the existing CO2 pipeline is not available, new pipelines can potentially be built utilizing existing pipeline ROWs to reduce the costs associated with obtaining new ROWs. Given that there are over thousands of miles of existing CO2 pipelines and millions of miles of natural gas pipelines in the United States, the new modeling capabilities developed in this work will play a key role in accelerating large-scale CCS deployment.
A Hybrid Approach for Production Forecasting and Uncertainty Characterization
Authors: Guo Zhenyu , Sankaran Sathish 
Published: September 2024

Abstract
RGNet (Reservoir Graph Network) is a pragmatic reduced-physics model that utilizes the essential physics of flow in porous media while simplifying computational complexities. Its versatility has been demonstrated through successful applications in a range of reservoir management applications, including reservoir connectivity analysis, resource volume estimation, dynamic forecasting, well control optimization, flood optimization, and integration with surface network models. While RGNet has been proven effective, there is a vision for further enhancing its modeling precision and predictive capabilities by incorporating advanced machine learning (ML) techniques.
In this study, we propose a hybrid approach which combines RGNet and ML for production forecasting with uncertainty characterization. The basic idea is to use RGNet-simulated data as training features and use sparse regression (SpR) to select the important features and fit a data-driven model between the features and the target output. To forecast into the future, we substitute the simulated data of the future period into the SpR model to obtain the corresponding forecasted target output. For training purposes, we only need a rough prior model to start with and the calibrated model is not required, which significantly reduces the manual effort in history matching. In addition, as the training and prediction are very fast to perform, the same procedure can be repeated for multiple prior realizations in quite short time, which enables the uncertainty characterization of the future performance.
The proposed hybrid RGNet-SpR method was tested on a deep-water field case to see whether it can improve the forecast performance of BHP. For training purposes, multiple prior RGNet realizations are randomly generated and simulated to generate the training features. The features considered relevant include simulated well-level injection/production rates and BHP data. As the simulation system are controlled using gas/water injection rates for gas/water injectors and using oil production rates for oil producers, these control variables as input features are also the historical production data in the training process. The output targets are BHP of target wells, which include multiple wells. As RGNet-SpR fits coefficients for different features, we use the magnitude of the features to characterize the strength of the connectivity between the target well and feature well. The proposed workflow is quite robust even when we use less than 50% of available data for training to obtain good quality forecasts. Also, multiple forecasts are shown to span the unseen data with a relatively small error band. Combining RGNet with ML provides a more robust and efficient solution for production forecasting with capability for uncertainty characterization. The proposed solution does not require a history-matched model as the base model, which saves resources and enables faster decision making.
Advanced Multi-Lateral Monitoring Using Intelligent Inflow Tracers with Production Log Comparison in a Carbonate Reservoir
Authors: Alshaer H. , Almansouri M. , Al-Harbi M. , Leung E. , Alghamdi M. , Al Haji L. , Huseby O. 
Published: September 2024

Abstract
Production logging tools (PLT) have been the incumbent of downhole monitoring technology for decades. These tools, often used in conjunction with tractors in extended laterals, pinpoint the locations of water or gas breakthrough and map the flow profile across producing zones. However, the logistics of scheduling and mobilizing production logging equipment and personnel are cumbersome and time-consuming. Acknowledging these challenges, alternative downhole monitoring technologies had to be explored to circumvent the limitations associated with PLT monitoring. The innovative approach involved deploying a smart multi-lateral well system with inflow control valves (ICV), annular and tubing pressure and temperature sensors. In addition, unique oil and water inflow tracers were installed to enable monitoring without intervention risks, with limited personnel and costs. The sensors were strategically placed near the junctions in the mother-bore. The primary objective was to conduct a blind comparison of inflow tracing and conventional production logging, assessing tracer applicability in a complex well and reservoir environment. The chosen well, a penta-lateral with extended reach, completed across multiple zones with different pressure regimes and high H2S, provided a challenging setting for this comparison. The study implemented monitoring campaigns under varied rate conditions, with and without PLT, to evaluate the downhole technologies’ performance. This paper presents novel and patented modeling methods designed to accurately interpret inflow tracer responses, especially where standard modelling approaches fall short. The blind comparison initiative, encompassing six distinct monitoring campaigns across different production scenarios in a carbonate oil reservoir, aimed to track lateral performance over time. The findings indicate that inflow tracer interpretations are comparably similar to PLT results, validating tracer methodology as a means to enhance and simplify well monitoring in complex well settings.
Novel Optimization of Diesel Fuel and Emissions Across Multinational Oil Rigs Through AI
Authors: Marzban A. , Grauer D. , Wright C. , Yoon J. , Sarna S. , Salib M. , Singh M. P. 
Published: September 2024

Abstract
Offshore and onshore oil rigs generate power from sets of diesel generators, which significantly contribute to upstream and drilling-related emissions. These generators often operate inefficiently due to the "all or nothing" fashion used to maintain a safety margin of power despite highly variable rig loads. This paper introduces a novel artificial intelligence (AI) framework that integrates data pipelining, machine learning (ML), and evolutionary optimization techniques to dynamically model and optimize the emissions profile of generator schedules based on rig load demand. Utilizing historical operations data from over 50 rigs worldwide, the framework forecasts power demand in real-time and classifies rig states to inform generator scheduling. This dynamic approach allows for load balancing, minimizing fuel consumption and greenhouse gas emissions by operating only the necessary generators at their best efficiency points (BEP). Comprehensive simulation results demonstrate a reduction in fuel consumption and emissions exceeding 20%, with some rigs achieving up to 30% savings. This innovative framework represents a significant step towards more efficient and environmentally responsible energy use in the oil and gas industry.
Evaluating Carbonate Reservoir Rocks for Underground Hydrogen Storage: A Comprehensive Laboratory Approach
Authors: Galvis-Silva H , Okoroafor E. R 
Published: September 2024

Abstract
Underground Hydrogen Storage (UHS) in porous media represents a promising solution for long-term, large-scale energy storage. Despite its potential, UHS development faces challenges such as low storage efficiencies and potential geochemical reactions between hydrogen and reservoir rocks. This study investigates the behavior and interactions of carbonate rocks, specifically limestones, under reservoir conditions for UHS, focusing on both geochemical and geomechanical changes induced by hydrogen exposure.
Static reactivity tests were conducted on various limestone samples at a constant pressure of 500 psi, exposed to different temperature conditions (25°C and 50°C) and brine over 30 days. Both dry and saturated states were tested to simulate diverse reservoir conditions. Parameters such as porosity, permeability, and Uniaxial Compressive Strength (UCS) were measured before and after exposure. Results showed that porosity remained stable across all conditions, suggesting hydrogen exposure does not significantly alter pore structure. However, permeability exhibited mixed responses, with increases observed in two out of three limestone types, indicating hydrogen exposure could enhance fluid flow in certain carbonate rocks.
All samples exposed to elevated temperatures and saturation conditions demonstrated a reduction in UCS, indicating a weakening of mechanical integrity, which could impact structural stability and safety of UHS operations. The extent of weakening varied among limestone types, suggesting mineralogical composition plays a crucial role in rock response to hydrogen exposure. These findings provide critical insights into the geochemical and geomechanical behavior of carbonate rocks under hydrogen exposure. While UHS in carbonate reservoirs appears feasible, the significant reduction in UCS highlights potential risks to mechanical stability. Understanding these changes is essential for assessing the feasibility and safety of UHS projects in carbonate reservoirs. Future research should focus on long-term exposure tests and the development of mitigation strategies to address the mechanical weakening of reservoir rocks.
A New Algorithm for Automated ISIP Interpretation
Authors: Garbino A. G. , Espinoza D. N. , Savitski A. A. , Mondal S. 
Published: September 2024

Abstract
In multi-stage unconventional horizontal wells instantaneous shut-in pressure (ISIP) is useful for the analysis of hydraulic fracturing and its interaction with the stress state in the reservoir. ISIP can be estimated from the leak-off pressure response after the end of the treatment. However, this value is normally eclipsed by pressure oscillations that occur immediately after well shut-in, known as water hammer, which complicate its interpretation. We developed an automated workflow to estimate ISIP values from fracture stages that allows for the utilization of various shut-in physical models. The workflow is based on guidelines proposed by Roussel et al. (2021) and incorporates the Trust Region Reflective algorithm to optimize all parameters in the model, which allows for model convergence in a wider range of cases and is validated using field data. Comparison of fitting model parameters between stages and the addition of a ‘goodness of fit’ metric allow for quick identification of outliers and poorly matched model predictions. ISIP estimations at different step-down rate managements were then compared for validation using a commercial hydraulic fracture simulator capable of reproducing water hammer. Simulations showed that ISIP interpretation is consistent at varying step-down rates. Additionally, water hammer frequency in each stage of the well was converted into depth to test its potential as a diagnostic tool for fracture stage isolation by comparing results to observations made through fiber optic measurements.
Easy Deployed Distributed Acoustic Sensing System for Leakage Detection in Gas Pipelines
Authors: Benabid M. K. , Garcia-Ceballos A. , Jin G. , Fan Y. 
Published: September 2024

Abstract
This study focuses on Distributed-Fiber-Optic-Sensing (DFOS) technology, a cutting-edge approach for monitoring the integrity of oil and gas pipelines. Unlike traditional inspection methods, DFOS offers continuous, real-time monitoring capabilities, allowing for early detection and response to potential leaks, which is especially crucial in remote or inaccessible locations. The current study investigates the innovative and practical use of this technology through the internal deployment of fiber-optic cables within the pipeline for gas leak detection.
To conduct our research, we used a standard industry pipe measuring 21 meters in length with a 4-inch internal diameter. The pipeline included a 1-meter section in the middle specifically designed for adaptability to various experimental setups. We tested the system at five different standard gas velocities (2, 6, 10, 14, and 18 m/s) and introduced leaks at the center of 1-m middle section in four sizes (¼", ½", ¾", and 1") at three different orientations (top, bottom, side). Our experimental setup is equipped with four distinct fiber-optic cable deployment methods: one external cable taped to the top of the pipeline, and three different cable types deployed internally. Our baseline experiment, conducted without any leaks, served as a reference to compare against the leak scenarios. We observed significant vibration anomalies at the leak locations, enabling us to not only detect but also accurately locate the leaks. The experiments revealed that leaks became more detectable as the size of the hole increased or as the velocity of the gas increased. Notably, the internal cables interacting with the flow, particularly the softer and lighter ones, displayed a higher sensitivity to these anomalies compared to the heavier cable, suggesting that the physical properties of the cables play a crucial role in detection capabilities. The leakage position also impacts detectability, being more detectable as it is closer to the cables. The external straight cable can also detect the leakage but is less sensitive compared to the internal light cables. The detectability of the external cable could be impacted by the coupling; improved coupling between the cable and the pipe can enhance its detectability of pipeline leakage.
The principal innovation of our study lies in the internal deployment of fiber-optic cables for detecting pipeline leaks. This method represents a significant shift from traditional external deployment methods, offering a more field-practical and efficient solution for pipeline integrity monitoring. By providing real-time data on the state of the pipeline from within, this approach could dramatically improve the industry’s ability to prevent leaks and respond to incidents, thereby enhancing safety and operational reliability.