bluuebunny/crossref_metadata_2025_split huggingface dataset:
Success Cases and Lessons Learned After 20 Years of Oilfield Digitalization Efforts
Authors: Saputelli L. , Duran J. , Rivas F. , Casas E. , Ramos L. , Bravo C. , Chacon A. , Temizel C. , Mubarak S. , Chacon J. , Escorcia A. , Elred M. , Diaz S. , Yrigoyen A. , Moricca G. , Querales M. , Lopez C. 
Published: September 2024

AbstractThe energy transition presents a critical juncture for the oil and gas industry. As we navigate toward a more sustainable future, digital oilfield (DOF) technologies play a pivotal role in optimizing operations, reducing environmental impact, and ensuring energy security. This paper aims to explore best practices and lessons learned from two decades of digital oilfield (DOF) implementations. The scope includes analyzing successful case studies, challenges faced, and practical guidelines for short-term DOF adoption in the context of the energy transition.Our approach involves a comprehensive literature review, examining industry reports, academic studies, and real-world experiences. We will synthesize insights from DOF projects, emphasizing data integration, change management, and interdisciplinary collaboration. The process includes distilling actionable recommendations for future implementation. Through our review, we anticipate uncovering key success factors and pitfalls in DOF initiatives. We will present practical observations on data governance, cybersecurity, and strategic alignment. Additionally, we aim to draw conclusions regarding the adaptability of existing DOF practices to the evolving energy landscape.Value creation from digital oilfield applications exceeded all our previous expectations. Over the past 20 years, DOF initiatives have evolved significantly. Notable achievements include the improved decision-making through streamlined processes and automated workflows; Real-time monitoring of equipment health, production, and safety; Holistic views of reservoirs, wells, and facilities for optimized field development; Efforts to standardize data formats and enhance interoperability; Leveraging cloud platforms and big data analytics for scalability and efficiency and Connecting sensors and devices for enhanced insights.We address the remaining challenges for full fledge adoption including prioritizing change management, training, and cross-functional collaboration, while fostering a learning culture to adapt to evolving technologies and industry dynamics. This paper contributes novel insights by bridging the gap between DOF and the energy transition. It provides actionable guidance for oil and gas professionals, enhancing their ability to navigate the digital frontier while addressing sustainability goals. By synthesizing existing knowledge, we aim to propel the petroleum industry toward a more efficient and resilient future.
Resolving a Flow Assurance Issue at Offshore Field: A Successful Novel Technology Deployment
Authors: Hendraningrat L. , Rodzali M. A. , Ramly N. A. , Riyanto L. 
Published: September 2024

Abstract
Solid scale like deposition is one of the most common flow assurance challenges, both subsurface and surface. Solid deposition above a certain limit may limit outflow and productivity. An offshore field in Malaysia has suffered mixed scales (wax and inorganic scale), which has decreased oil production. As observed from the well site, the recovered sample was retrieved from the tubing in the wellbore. This paper presents a successful deployment of in-house novel technology to resolve a potential flow assurance issue due to mixed scales in the offshore field.
The mixed scales build-up over time and eventually reduces the effective flow path leading to a flow restriction or eventually a blockage of the line. Mixed scales deposits in tubing reduce the inner diameter and flow rate. Eventually, the wax forms a blockage, prohibiting oil production. Comprehensive analysis and evaluation both in the laboratory and well were carried out, integrated with thermodynamic modelling prior to novel technology deployment in the field.
The loss of ignition test revealed that more than 99% of the deposit is an organic material and the rest is inorganic. PVT fluid data was characterized using the EOS model, and fine-tuned fluid properties achieved an acceptable match with PVT experimental data as a validation process. The comparison of wax content between laboratory measurements and model prediction is acceptably accurate. Laboratory measurements and model predictions match wax content. The in-house chemical treatment was developed and successfully dissolved mixed scales overnight, according to dissolution tests. The compatibility test with existing production chemicals such as scale inhibitor (SI), corrosion inhibitor (CI), and wax inhibitor (WI) was also performed, and the in-house chemical had no incompatibility issues.
This study demonstrates how vital and necessary proper with thermodynamic modelling analysis is prior to well put onstream to anticipate any flow assurance issues that may cause decreased and prohibited oil production. Simultaneously, this paper successfully identified the suitable chemical treatment to control mixed scales in this field.
A Comparative Analysis of Completion and Reservoir Data to Decipher Productivity Drivers in North American Tight and Shale Plays
Authors: Indina V. , Singh H. , Liu Y. , Gu Y. , Song H. , Li C. , Zhang D. Leslie , Kong F. , Cheng P. , Li Z. , Li W. 
Published: September 2024

Abstract
The primary objective of this study is to thoroughly assess the influence of various completion, fracturing stimulation, and intrinsic reservoir properties affecting productivity of ten major unconventional oil and gas plays while uncovering key insights and emerging trends unique to each play.
We examined a dataset comprising of 91,519 horizontal wells that began production on or after January 1st, 2015, across ten major unconventional oil and gas plays (Bakken, Delaware, Duvernay, Midland, Eagle Ford, Scoop|Stack, Haynesville, Marcellus, Montney, and Utica) in North America. The analysis centered on four main aspects: well fracturing, downhole consumables, production, and geological properties. The horizontal and vertical well spacings were based on the horizontal and vertical distance between the target wellbore and its closest near neighbor in any zone, respectively. The wells were classified as parent, child, or co-completed based on the distance and timeframe between their completion. The Bakken-US play shows the most remarkable increase in normalized production per 1000 ft of lateral, almost doubling from 2015 to 2024, while Scoop|Stack experienced a decline in normalized production. Bakken and Montney, with high TOC, had low normalized productivity, whereas Utica, despite low TOC, was highly productive, underscoring completion quality's importance over organic quality. Although high brittleness is usually seen as beneficial for promoting fracturing, it is intriguing to note that plays with fewer wells per pad (e.g., Scoop|Stack) exhibit a higher growth rate of fractures, even when they have a lower brittleness index (median &lt; 0.34). Horizontal spacing plays a crucial role in optimizing performance. Less productive plays benefit from denser development. Co-completed wells outperform other sequencing, indicating their ability to mitigate "frac hits" or fracture-driven interactions. Longer laterals offer better resource contact, but productivity does not increase linearly. Sand remained the predominant proppant used in all plays due to cost-effectiveness. Freshwater remains the dominant frac fluid across all plays, and slickwater frac jobs consistently yield high productivity. The type of chemicals in terms of their dominant use varies across plays, but the trend in each play is largely unchanged since 2015. The number of clusters per stage varies across plays, and increasing the number of clusters may not always lead to enhanced productivity due to the stress shadowing effect.
This study offers important insights into factors affecting productivity in shale plays, aiding future well development and resource extraction optimization. It provides technical guidance for unconventional oil and gas developments in North America and is a useful reference for similar projects globally, including in China.
ConGANergy: A Framework for Engineering Data Augmentation with Application to Solid Particle Erosion
Authors: Zhang Jun , Li Yijie , Pei Weiping , Shirazi Siamack 
Published: September 2024

ABSTRACT
Erosion is a phenomenon involving material removal due to fluid entrained particles impinging on material surfaces. Experiments are crucial to investigate erosion severity and the impact of different physical parameters. However, conducting these experiments is expensive and time-consuming, and collecting high-quality data is challenging due to the small scales of material removal and sensitivity to external environment changes. Over the years, research at the Erosion/Corrosion Research Center (E/CRC) at the University of Tulsa has focused on investigating solid particle erosion in pipelines and fittings. Experiments are conducted using a large-scale boom tower flow loop, and erosion data is collected with high-accuracy, temperature-compensated ultrasonic transducers. This has resulted in more than 200 sets of high-quality experimental data covering various pipe diameters, particle diameters, sand concentrations, and flow velocities. Mining the inherent patterns and physical laws within this data is valuable for guiding future process condition selections and experimental tests. With the existing data covering a wide range of conditions, machine learning methods can be utilized to augment the data, ensuring the newly generated data adheres to physical laws and has similar distributions. Conditional Wasserstein Generative Adversarial Networks (CWGANs) are a class of machine learning models designed for generating conditioned data. CWGANs can be applied to generate additional training data when the available data is limited, significantly expanding the erosion databank for subsequent mechanistic modeling. This work proposes the Conditional GAN Synergized SPPS model (ConGANergy) framework for engineering data augmentation, which utilizes CWGANs to expand erosion experimental datasets. We conducted experiments to evaluate the performance of the proposed framework by comparing the newly generated data against original experimental data and data obtained through a semi-mechanistic erosion prediction model Sand Production Pipe Saver (SPPS) developed at E/CRC. This approach provides engineers and researchers with a powerful tool to expand databases and build robust mechanistic models, leveraging the inherent patterns within high-quality experimental data while adhering to the physical laws governing erosion phenomena.
Laboratory Validation of Microannulus Repair Using Nanosealant Technology: Pre- and Posttreatment Logging and Permeability Characterization
Authors: Kragset S. , Govil A. , Beltrán-Jiménez K. , Jain B. , Gardner D. , Ford E. P. , Taheri A. 
Published: September 2024

Abstract
With aging oil and gas fields, permanent plug-and-abandonment activity is becoming significant and requires solutions capable of repairing leak paths. The use of nanosealant technology to treat defects in annular cement was evaluated using ultrasonic logging and permeability tests on test cells. A repeatable testing procedure was developed to validate the usability of the nanosealants to repair cemented annular barriers.
Two identically constructed test cells were fabricated with 7-in. and 9 5/8-in. casings filled with standard Class G cement. Pressurization of the inner casing during cementing enabled a controlled debonding of the annulus cement and the 7-in. casing upon relieving the pressure after the cement was cured. Permeability tests with water and ultrasonic logging measured the baseline state for the test cells in dry and wet conditions before treatment with a nanosealant solution. After treatment, permeability testing and ultrasonic logging in dry and wet conditions were repeated, and the results were compared with the baseline measurements.
The test cells were shown to have equivalent properties with respect to permeability and debonding and demonstrated along with related experiments that a consistent and repeatable procedure for the qualification of microannulus treatment materials can be followed. The nanosealant was able to penetrate a microannulus with an initial hydraulic aperture of about 50 μm, and the permeability measurements showed that the permeabilities were significantly reduced in both cells after the treatment. Ultrasonic logging using pulse-echo and flexural measurements results indicated improvement in the bond quality. Even though nanosealant has been used successfully in the field, this work represents the first comprehensive laboratory validation of the product supported by logging measurement and provides insight into the nano-sealant activation mechanism.
A procedure for consistent fabrication of test cells for qualification of microannulus treatment materials was demonstrated. A multidisciplinary team developed a methodology that combined physical flow measurements with ultrasonic measurements to validate the performance of a nano-sealant system and confirm the coverage of the microannulus.
Two-Step Process to Quantify Effects of Geological and Completion Parameters on Unconventional Well Performance
Authors: Kelkar M. , Itteyra A. 
Published: September 2024

Abstract
Machine learning algorithms have been frequently used to evaluate the performance of unconventional wells. Typically, all available features (geological and completion) are used to correlate the data to production performance of wells. Unlike the traditional approach, we correlate production data first to geological and geophysical parameters (depth, porosity, permeability, brittleness, etc.). We then take the residual information after the first correlation and correlate the residual to completion parameters such as length, proppant amount and fracturing fluid. By separating geology from completion, we can isolate the effect of geology removed from completion. This is very useful for exploration prospects where very limited or no data are available from existing wells. Further, by correlating completion parameters separately to residual values, we can quantify the impact of each completion parameter which can help optimize completion parameters to get the most benefit of various completion strategies. We use SHAP (Shapley Additive Prediction) to isolate the effects of each completion parameter.
Using large, confidential, data available, we evaluated multiple basins and applied the proposed methodology. These include: Eagleford, and Marcellus Basins. What we observed, in general, is that approximately 70 % of the variance in production data can be explained by geological parameters and about 20 % of the variance can be explained by completion parameters. True vertical depth consistently ranked as the most important geological feature in predicting performance, but other features varied in importance. If appropriate analog can be understood, the methodology from mature formations can be used in exploration blocks to identify sweet spots. The evaluation of completion parameters revealed that the relationship between performance and completion is not always linear and in some instances, incremental benefits of larger completion inputs are negligible and counter-productive. The length of the well is the most important completion variable in determining the performance. The impact of individual parameters varies depending on production parameter selected. Geology becomes increasingly important as more production data are added (12 month vs 24 month vs EUR) and completion parameters are less able to explain the performance as the length of the production data increases.
Active Magnetic Ranging from Bottom Hole Assembly: Tool Performance
Authors: Ytrehus J. D. , Husby K. , Saasen A. , Hjelstuen M. , Eriksen T. J. 
Published: September 2024

Abstract
A well intercept operation has the scope to drill into an existing well. Similarly, some infill drilling operations are conducted with the scope of avoiding interception with other wells. A prototype of a tool for Active Magnetic Ranging While Drilling (AMR) without the use of a wireline operation has been developed for use in petroleum and geothermal wells. The main scope of the current article is presenting the results of application of this new tool in a test well.
The ranging tool emits a low frequency alternating current into the formation to reach the target well and then run through that's casing and returning back to the well being drilled. This current set up a variable magnetic field that is measured by the AMR tool determining the direction towards the target well as well as the distance. If drilling a relief well, 10 - 25 wireline runs are needed before the target well is intercepted. The present tool perform similar operations with an integrated tool systems in the drill pipe and, thus, avoid all the tripping operations.
A prototype of an active magnetic ranging tool on the drill pipe has been developed. This tool is outlined in detail in the paper. Focus will be given to performance of the tool. The tool and the progress have been described in earlier SPE papers. The current article describes the most recent results of the test well performances. The set-up and the results of the logging operation conducted on a drill pipe is described in detail. It is shown how the direction and distance between the two wells are measured using the tool. It is shown by computer simulations how these measurement results can be improved.
Most intercept operations are not relief well drilling, but cases where a well needs to be intercepted because a well section shall be connected to another well, or during plug and abandonment operation. The tool can also be used for avoiding collision with other wells, which is a relevant scope for drilling infill wells in older fields or radiator wells in geothermal drilling. The tool and its potential are outlined in the paper
Transfer Learning in Subsurface Flow Surrogate Model with Physics-Guided Neural Network
Authors: Cheng H. B. , Qiao J. H. , Wei Y. C. , Li S. C. , Zeng P. , Yu H. B. 
Published: September 2024

Abstract
It is a great challenge for reservoir engineers to accurately and quickly model the subsurface flow surrogate for oil and gas reservoirs. The traditional numerical simulation methods are high computational complexity and time-consuming. The widely used pure data- driven flow surrogate methods require massive high-quality data due to lack of theoretical foundation. Therefore, it is necessary to develop transfer learning methods integrating prior knowledge with data-driven deep learning methods to solve complex seepage problems. To reduce data requirements and improving modeling efficiency, a theory-guided and data-driven transfer learning method is proposed to build the fast and accurate subsurface flow surrogate model. Specifically, a data-driven machine learning method is first proposed to simulate and predict the flow processes and build the primary surrogate model. In addition, physical mechanism and constraints are embedded into the data-driven model to make the prediction results satisfy the prior domain knowledge. The transfer learning method based on physics-guided neural network (TL-PG) integrates the seepage theory with sparse spatial data to improve the prediction accuracy of the surrogate model. The proposed TL-PG method is verified by a subsurface flow problem in heterogeneous reservoir models. First, we build a flow field to simulate 50 years of production history. A physics-guided neural network model is trained based on the samples from the first 30 years and tested based on the last 20 years of data. The relative L2 loss and the coefficient of determination R2 are used for comparison. Compared with the purely data-driven method, the relative L2 loss of physics-guided neural network is increased by 16%, and the mean coefficient of determination R2 is up to 0.8932, which means the embedded physical mechanism can greatly improve the performance of neural networks. Secondly, the physics-guided neural network is selected as the pre-training model. Then, the random points from the year 31 to 40 are used to fine-tune the model. Finally, the TL-PG surrogate model is validated by the last 10 years of data. The mean coefficient of determination R2 is up to 0.9522, which has improved by 25% compared to purely data-driven deep neural network (DNN) method, and only when the number of training data and epochs are increased several times, the performance can be comparable to the TL-PG method. However, the training time of DNN is more than 3 times that of the proposed method. The results show that the transfer learning surrogate model can effectively capture the feature of subsurface flow fields and greatly reduce the demand for data while significantly increasing computational speed.
An Automated Workflow for Condition Monitoring of Centrifugal Compressors Using a Combined Data-Driven and Physics-Based Approach
Authors: Santiago C. J. S. , Abbas H. , Thangamani P. 
Published: September 2024

Abstract
Equipment degradation is inevitable during operation. The breakdown of equipment is costly due to replacement expenses and may cause a facility to shut down, leading to higher costs and production losses. Hence, equipment maintenance is essential to ensure a satisfactory level of reliability during its lifecycle. Maintenance strategies may adopt a reactive approach, in which maintenance happens after failure and should be avoided, or a preventive approach, in which maintenance happens regardless of the state of the system.
This work aims at addressing the challenges posed by system deterioration and costly downtime through the development and implementation of a condition monitoring solution for a centrifugal compressor situated in an offshore platform. To achieve this objective, a comprehensive methodology was developed containing two modules: fault detection and prognostics. In the fault detection module, a hybrid approach was adopted by combining measured and simulated data generated using physics-based models, where a machine learning model is trained to detect anomalies and generate alarms. In the prognostics module, a degradation state variable is introduced to predict the time to failure of the centrifugal compressor.
By leveraging process simulation to construct a physics-based model, the system can track changes in design polytropic efficiency using real-time sensor data to make predictions of time to failure using a Kalman filter algorithm. We demonstrate the methodology through examples using actual centrifugal compressor measurements collected from Valhall public industrial data. The fully automated solution provides real-time insights on the operational state of the centrifugal compressor, enabling proactive maintenance strategies. The analysis of centrifugal compressor sensor data is streamlined to detect anomalies and trigger alarms for different sensors, enabling prediction of time to failure within a few days before the compressor shutdown happens.
The approach presented in this work integrates the strengths of both physics-based and data-driven methods. The methodology exploits the alarm generation and complements it with a time-to-failure prediction using a Kalman filter algorithm. The presented solution contains a real-world application of machine learning in condition monitoring and has the potential to enhance predictive maintenance planning and prevent costs associated with downtime.