bluuebunny/crossref_metadata_2025 huggingface dataset:
First Global Deployment of Level-5 System with Dual ESP in Offshore
Authors: Alsaihati Zainab , Balseiro Prudencio , Juaid Faisal , Alghamdi Mohammed
Published: September 2024

This paper presents the success story of the first worldwide implementation of a Level-5 multilateral completion system, fiber optics permanent downhole monitoring system (PDHMS), and dual electrical submersible pump (ESP) upper completion in an offshore environment.

The project took place on an offshore dual-purpose evaluation and producer well consisting of a pilot hole and a horizontal lateral across the reservoir section. The pilot hole was drilled with a maximum inclination of forty-five degrees, where required logs were collected. Then, a seven-inch liner was set and cemented to enable future reservoir surveillance. Successful deployment of complex completions requires meticulous planning among a multidisciplinary team. Preparation for logistics and standardization of the operating procedures among all involved parties was an essential key to the success of the project.

The state-of-the-art completions enabled full access to the pilot hole for future intervention to monitor the reservoirs behind the seven-inch liner in the pilot hole. Additionally, the Level-5 system provides a hydraulic isolation of the lateral junction to avoid potential water cut during the production phase. Furthermore, the purpose of completing the well with dual ESP is to extend the life of ESP by alternating the ESP operation periodically, which will, consequently, minimize workover intervention to replace ESP. In addition, the most beneficial advantage of using dual ESP completion is that it prevents shutting down the oil production while waiting on a workover rig to replace ESP.

The novelty of the completion described in this paper is the combination of running a Level-5 completion system, fiber optics PDHMS, and dual ESP with upper and lower Y-tools. Such utilization of intricate tools in one well was unprecedented globally.
Prediction of Minimum Miscibility Pressure Between CO2 and Crude Oil by Integrating Improved Grey Wolf Optimization into SVM Algorithm
Authors: He Youwei , Zhao Guoqing , Tang Yong , Rui Zhenhua , Qin Jiazheng , Yu Wei , Patil Shirish , Sepehrnoori Kamy
Published: September 2024

CO2 injection can enhance oil recovery and achieve geological carbon sequestration. The miscibility between CO2 and crude oil significantly impacts the CO2 EOR performance. Although the minimum miscible pressure (MMP) can be obtained by slim-tube experiment or slim-tube modeling, it is time-consuming, inconvenient, and complicated. This work aims to enhance the prediction efficiency and accuracy of MMP between CO2 and crude oil under reservoir conditions by improved and integrated machine-learning approaches.

A novel method is proposed to improve the forecasting accuracy and efficiency of the MMP by integrating Grey Wolf optimization (GWO) and improved GWO (IGWO) into the Support Vector Machine (SVM) algorithm. Firstly, data sets are collected and data preprocessing is performed to improve the quality of data sets. Secondly, K-fold cross-validation is applied to enhance the generalization of the model. The MMP is predicted by the SVM algorithm. Thirdly, the MMP prediction can be enhanced by introducing GWO and IGWO algorithms, and the optimal model is investigated to evaluate the effect and convergence of the SVM-GWO and SVM-IGWO algorithms. Fourthly, the predicted MMP and evaluation index (MAE, MAPE) are compared. Finally, the field case study is performed to show the practical potential of the approach.

The dominant factors of the MMP include formation temperature (TR), MwC5+ (Molecular weight of pentane plus), MwC7+ (Molecular weight of heptane plus), Volatile (mole fraction of volatile components including N2 and CH4), and Intermediate (mole fraction of intermediate components including CO2, H2S, and C2–C4). The data set is formed by filling 87 groups of missing values using the K-Nearest Neighbor (KNN) algorithm and removing 19 groups of outliers based on the Box-plot detection method. The accuracy is improved by 37.45% and 40.79% using GWO and IGWO based on the MAE compared to SVM. The calculated MAPE shows that the accuracy can be enhanced by 37.79% and 41.29% after adding GWO and IGWO. The SVM-GWO and SVM-IGWO improved the accuracy by 54.16% and 57.12%. The proposed method can accurately determine the MMP between CO2 and crude oil. The field case study highlights the reliability of the proposed method. The developed method can forecast the MMP between CO2 and crude oil more efficiently and economically.
Estimating Petrophysical Properties Directly from Seismic: A Deep Learning Application to Carbonate Field for CO2 Storage Potential
Authors: Lew C. L. , Fuad M. I. Ahmad , Jaya M. S. , Trianto A. , MacBeth C.
Published: September 2024

Geological carbon capture and storage is vital for reducing carbon dioxide (CO2) emissions. Carbonate Field 1 in Luconia Province, offshore Sarawak is a potential CO2 storage site. Porosity and clay volume (Vclay) estimation from seismic provide valuable spatial and temporal information in characterizing reservoir distribution and overburden for assessing containment integrity and storage capacity. A deep learning inversion method for simultaneous estimation of porosity and Vclay was applied and tested in Carbonate Field 1. UNet architecture, chosen for its ability to preserve spatial resolution, processes post-stack seismic as input and petrophysical properties as outputs. Mean-squared error is implemented as the loss functions during the training on synthetic dataset. We use facies-based geostatistical simulation to generate 1D synthetic petrophysical logs. The petrophysical properties are linked to elastic properties through a carbonate rock physics model and fluid substitution using Gassmann's equation. The computed elastic properties are then used to calculate the angle dependent reflectivities (0°-30°) using the full Zoeppritz equations. The reflectivities are convolved with possible source wavelets derived from the seismic post-stack to generate the synthetic seismograms. These synthetic seismograms are then averaged to obtain a single noiseless synthetic seismic data, prior to addition of estimated field noise. 40,000 realizations of 1D synthetic datasets are generated for the training dataset. An equal distribution of 4000 realizations is allocated for both validation and testing datasets. Each 1D synthetic dataset consists of 128 samples, with a sampling rate of 4ms. The trained network model is applied to the testing data which has not been seen by the network during the training. Using Pearson correlation coefficient (cc) as the metric to evaluate the prediction performance, the model provides promising result (cc>0.80) for the estimated porosity and Vclay when evaluated on the testing data. Application on the field dataset of Carbonate Field 1 demonstrates satisfactory prediction performance, with cc value exceeding 0.65 and 0.85 for both the estimated porosity and Vclay respectively. Low (<0.2) and high (>0.5) estimated Vclay content is interpreted as carbonate and shale respectively. The findings allow interpreter to characterize the heterogeneity of carbonate depositional facies and quality by integrating the estimated porosity and Vclay for CO2 storage planning. The process of estimating the porosity and Vclay directly from seismic using the deep learning approach takes one week, which include training data preparation and implementation.
Two-Way Coupled Flow and Geomechanics Simulation with Advanced Dual-Porosity and Dual-Permeability and Local Grid Refinement for Shale Primary Depletion
Authors: An Jaewoo , Yang Daegil , Chen Jianping
Published: September 2024

This paper represents a new two-way coupled flow and geomechanical simulation model that utilizes an Advanced Dual Porosity Dual Permeability (A-DPDK) model with local grid refinement (LGR). A hydraulic fracture's conductivity is strongly dependent on the effective stress on fracture face. During production, increased effective stress could cause proppant embedment, crushing, and fracture closure. Coupled flow and geomechanical simulation is critical to capture the fracture conductivity changes with stress and perform accurate production forecast. The geomechanical problem with the DPDK can be solved with the constitutive equations for stress and porosity of multiple porosity systems. A fractured reservoir contains different regions in the fracture domain, for example, propped, unpropped, and natural fractures which have different mechanical properties. Our proposed model mathematically represents mapping of mechanical properties in different regions of fracture and matrix domains. In addition, we showed the mapping between a LGR flow system and a non-LGR geomechanical system. We demonstrated a realistic fracture geometry with the A-DPDK LGR system. We validated the numerical convergence of the A-DPDK with LGR by performing grid size sensitivity tests in flow and mechanical grids. The proposed simulation model was applied to oil production from a shale and tight (S&T) reservoir. The proposed simulation approach can model total stress update near fractures more realistically. The new stress and pressure results obtained from the integrated flow and geomechanics workflow allowed us to incorporate stress impact from the depletion of a different bench and the resulting fracture azimuth change. The combination of a LGR flow system and a non-LGR geomechanical system supports reasonable computational time even with two-way coupling simulation, which encourages us to adopt the new simulation approach for modeling oil production and evaluating stress change.
Sanding at Low Oil and Water Flux in Cased and Perforated Wells in Weak Formations
Authors: Xiao Y. , Vaziri H. , Giuliani C. , Guliyev I.
Published: September 2024

Production histories of five cased and perforated (C&P) wells from four oil fields are presented to show that sanding in C&P wells in weak formations is limited and manageable when the oil and water flux is low. That observation is explained through a finite element numerical model that considers the sanding mechanisms at scale. The fluid flux in this paper is defined as the fluid flow rate per foot length of the flow contributing intervals on the sandface and it dictates the fluid viscous drag. A high flowrate well may still have a low fluid flux if its completed and flow contributing intervals are long. Based on the reservoir formation's unconfined compressive strength (UCS) and the average size of individual sand grains (D50), the five wells are categorized into three groups. The Group-1 well has UCS of slightly above 1000 psi and a D50 of 150-250 microns, the Group-2 wells have UCS of 500-1000 psi and a D50 of 150-250 microns, and the Group-3 wells have UCS of less than 500 psi and a D50 of 70-150 microns. Although all those weak reservoir formation intervals disaggregated in the near-wellbore area from the beginning of their production life, there exists an oil-water flux range within which sanding is limited. Given that sanding is triggered once the fluid drag dominated by the viscous drag exceeds the frictional resistance of sand grains, the limited-sanding oil-water flux range increases with D50 as the larger the D50, the higher the frictional properties and the lower the fluid viscous drag. When D50 is below 110 microns, that flux range becomes quite narrow. Also, a sandface gas flow reduces the limited-sanding oil-water flux range, depending on the frictional properties and D50 of sand grains and the gas flux. Those findings are in contrast to a widely held view that sanding in a C&P well in a weak formation with watercut is severe and unmanageable, and thus an active sand control completion is required. The results of this study provide the technical assurance needed to deploy a simpler and more cost-effective non-active sand control completion in low fluid flux wells where an active sand control completion may be economically and/or technically unviable.
Innovative Redefinition of Well Cementing Utilizing Red Mud
Authors: Mahmoud Ahmed Abdulhamid , Elkatatny Salaheldin
Published: September 2024

This research delves into the incorporation of red mud, a by-product of the aluminum manufacturing process, into the cementing of oil wells to bolster the sustainability efforts of the oil and gas sector. A variety of experimental techniques were utilized to mix cement slurries by combining Saudi Class G cement with red mud at a 90/10 ratio. The objective was to assess the viability of utilizing red mud as a sustainable substitute in cement mixtures while ensuring the quality of the final product. The investigation included a comprehensive analysis, adjustments to viscosifying agents, and the addition of retarder, all aimed at achieving a cement slurry with a density of 15.8 ppg. Rigorous evaluations were made on the slurry's rheological characteristics and thickening time. Initial findings suggested that mixtures solely composed of Saudi Class G cement and red mud fell short of industry standards. However, with the precise addition of retarders and viscosifiers, the slurry's performance was significantly improved, meeting the stringent requirements of oil well cementing. These results indicate that red mud can support environmentally sustainable cementing practices without sacrificing performance. Therefore, this study recommends red mud as a sustainable and responsible material for the oil and gas industry, promoting sustainability while maintaining the necessary performance attributes.
Deploy Distributed Real-Time Data Pipelines Across Cloud and Edge to Process Rig Data for Easy Data Fusion and Processing
Authors: Wang A. , Acosta P. , Whitney R.
Published: September 2024

The need for the distributed real-time data pipeline arises from the requirement to process disparate, asynchronous data streams that emanate from edge devices deployed to remote field locations, like drilling rigs, and in the cloud. These data streams need to be combined in real-time to serve advanced analytics applications like Artificial Intelligence (AI) and digital twins. Because of its distributed nature, this data pipeline not only needs to process data but also manage deployment, containers, and physical and virtual computing appliances. Due to this complexity, today's distributed real-time data pipelines are nearly all built as custom software solutions, incurring expensive development costs, long development time, and introducing an extensive maintenance burden. This paper presents a new framework that builds the data pipelines as interconnected, graphical functional blocks in a single software user interface. Each functional block can be assigned to execute in multiple remote locations. Furthermore, each functional block can be parametrized to support unique properties of each rig. This architecture makes it significantly simpler and faster to build, deploy and scale distributed real-time data pipelines while accelerating data engineering and data science projects that require high-speed, high-volume, distributed real-time data.
Unlocking Well Start-Up Uncertainties with Transient Well Flow Modeling
Authors: Descousse A. , Quintero C. G. , Veiga H. , Hastings J. , Kilic A. , Babayeva L. F.
Published: September 2024

The Absheron field is located offshore Azerbaijan in the Caspian Sea about 100km south-east of Baku and 25km north-east of Shah Deniz field, with a water depth of approximately 500m. After a successful exploration well in 2011 and several development studies, production started in July 2023 through an Early Production Scheme (EPS). This EPS consists of a single subsea well, drilled in 2019, and a 35 km insulated production line going to the existing Oil Rocks facilities.

To anticipate the start-up behavior of the Absheron well, Transient Well Flow Modeling simulations integrating the well and the flowline were performed. As such simulations include hydrodynamics (conservation laws for mass, momentum and energy), thermodynamics (fluid description) and thermal exchanges with surroundings (architecture, annulus fluid, rocks…), they enabled the evolution of rates, pressures and temperatures to be predicted for the well ramp-up, in particular to establish the hydrates management procedure. Sensitivities were performed to account for uncertainty on well productivity (from the initial testing and logging data), the wellbore content, chemical injection rates and network pressure.

This paper describes the successful work achieved for unlocking challenging uncertainties of Absheron offshore high-pressure gas condensate well start-up. The various sensitivities performed allowed to properly anticipate well behavior. The simulation of several well start-up sequences based on in-house experience, as well as discussions with local offshore teams in charge of operations, helped in establishing operating guidelines grounded on shared understanding and highlighted the main risks linked to the start-up phases. This therefore facilitated the confirmation of the well ramp-up strategy including the expected pressure evolution both at wellhead and at reservoir wellbore interface, as no rate monitoring was available subsea. This ensured well integrity and effective well production. The criticality of hydrates prevention for this high-pressure gas system was handled by optimizing hydrate inhibitor injection rates and flowline pressure.

The work performed for this well highlights the benefits of integrated and transverse work and modeling through the integration of both well and flowline to establish optimal and flexible start-up guidelines.
MWD and Gyro Survey Tool Quality Management System
Authors: Noy K. A. , Elshabrawy M. S. , Bezemer J. J. , Appelboom R. S. , Albloushi A. M. A. , Al-Ajmi M. F. , Hassan Y. A. , Al-Ajmi S. A.
Published: September 2024

This paper describes a Measurement While Drilling and gyro survey tool quality management system. A first objective of the system is determining a definitive survey, by use of real time quality control parameters. A definitive survey is meant to have a quality that is meeting its theoretical or planned and approved wellbore position uncertainty. Further, field examples of the system application are presented and reviewed. A second objective of the system is delivering a self-contained reference for survey data quality management containing a quality control process, quality control procedures and quality control tolerances.
Enhancing Sustainable Oil and Gas Operations: Produced Water Treatment Using Biological Oxidation
Authors: Shah Karan Dinesh , Conway Bryce
Published: September 2024

In navigating the imperative of sustainable practices, the oil and gas industry faces a growing need for efficient and environmentally conscious produced water management. Challenges in produced water disposal encompass regulatory compliance, environmental considerations, induced seismicity risks, and the pursuit of economically sustainable solutions. There is a compelling need to explore efficient treatment methods that encourage the reuse of produced water while simultaneously providing long-term storage solutions to alleviate the strain on freshwater resources.

Biological oxidation, employing microorganisms to metabolize organic pollutants, has emerged as a promising and sustainable approach for produced water treatment. Applied at the secondary treatment stage, this technology significantly reduces contaminant and suspended solid concentrations post-primary treatment in the tank battery. The aim of this treatment method is to decrease the biological load in the produced water by introducing facultative microbes that prevent the pit from souring and halting H2S production. Additionally, aeration of the produced water impoundment is essential to introduce dissolved oxygen (DO) and enhance the performance of robust microbial strains.

The introduction of aeration proved instrumental in enhancing produced water treatment, facilitating the efficient dispersion of chemical amendments within impoundments through enhanced recirculation and mixing. Biological oxidation treatment resulted in below detection levels (BDL) of H2S, and post-treatment analysis revealed the absence of sulfate-reducing bacteria (SRB) activity, preventing souring in saltwater disposal (SWD) compared to conventional biocide or flop and drop treatments. Key indicators, such as dissolved oxygen and oxygen reduction potential (ORP), crucial for water stabilization, remained consistently stable over a six-month period without requiring further intervention.

The introduced biological oxidation process represents a cost-effective solution for treating produced water from diverse oil and gas extraction operations. A notable advantage is the absence of chemical or polymer sludge residue in the water impoundments, contributing to a cleaner treatment outcome. Beyond immediate benefits, biological oxidation plays a pivotal role in achieving long-term stabilization, fostering an efficient reuse program, and presenting a viable alternative to conventional water disposal methods.
Stimulation Design Improvements for Optimized Well Performance in Shallow Coal Bed Methane Wells
Authors: Pandey Vibhas J. , Flottman Thomas , Ganpule Sameer , Dewar Steven , Renwick-Cooke Tristan , Yang Shuyan , Hage Andrew , McAloon Corey
Published: September 2024

The wells completed in shallow coal seam gas reservoirs of Surat basin, eastern Australia, have been fracture stimulated for more than a decade with ever evolving treatment designs. In a recent fracturing campaign, critical design changes led to improvements in well performance and in mitigating issues related to proppant flow back and fines production. The paper discusses the improvements in treatment designs that helped achieve these favorable results.

The Jurassic age Walloons coal measures present in Surat basin were formed as a part of an extensive intra-continental sag basin where the non-pay interburden is mainly composed of water sensitive layers of mudstones, siltstones and reactive clays that often disintegrate when exposed to non-native water. The wells are completed in multiple stages using coiled-tubing based pin-point stimulation that targets various coal seams with small average thickness of 0.4 m (1.31 ft). The treatment designs are intentionally varied to account for varying stress regimes from normal to reverse stress regime as a function of depth.

The fracture treatment deign improvements were aimed at accelerating dewatering, improving the well performance, and mitigating proppant flowback while remaining constrained on total proppant volumes earmarked for the treatments. The major changes included reduction of pad volume, lowering gel viscosity, increasing maximum proppant concentration, re-arrangement of proppant stages and volumes in pumping schedules, introduction of rate-ramp to contain vertical fracture growth, packing the proppant to generate enough net pressure for consolidation, and dewater/flowback using new guidelines.

The new treatment designs aimed at improving performance successfully demonstrated the need for constant evolution of treatment parameters to counter the challenges observed in the field development process. Often, the solutions to typically occurring problems can be found by altering the approach and testing engineering limits of designs. The approach adopted in this study can be readily implemented on similar completions worldwide.
CFD Modelling of Multiphase Flow in Horizontal Pipe Annulus: Effect of Sweeping Pills on Drill Cuttings Removal
Authors: Aliyeva T. , Czuprat O. , Byrski P.
Published: September 2024

Effective hole cleaning plays a crucial role in drilling operations, especially with the ever-increasing number of deviated and horizontal wells. Sweeping pills are commonly pumped into the annulus to assist in cleaning of borehole from cuttings that have not been removed after the usual drilling fluid circulation. Hole cleaning becomes even more challenging with increasing inclination since the cuttings tend to settle down to the lower side of the annulus due to higher gravity effects.

The sweeping performance of different pills has been studied by taking an advantage of computational fluid dynamics (CFD) approach and subsequently, confronting the results with the outcomes of experimental work conducted previously [SPE 201634]. Multiphase flow simulations have been carried out where varying combinations of pill type, cuttings diameter, and inner pipe rotation were tested to study their influences on the final cuttings recovery. The results obtained from CFD simulations are compared to the experimental ones with the aim of catching the consistencies and discrepancies in the observed flow behaviors. Furthermore, the insights gained emphasize the intricate interplay between inner pipe rotation and the effective suspension & transport of cuttings, shedding light on the nuances that can impact drilling efficiency.

Furthermore, the study offers valuable recommendations for future research directions as well as field implications.
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

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

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

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

Carbon 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

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

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

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

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

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

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.