bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of Perforation Efficiency in Gas Well Using Machine Learning
Authors: Tejo Sukotrihadiyono , Amega Yasutra , Dedy Irawan 
Published: October 2021

Abstract
The efficiency of perforation is an important aspect in gas well since it affects near wellbore pressure drop related to turbulent flow. The perforation efficiency is correlated with non-Darcy skin that is able to be distinguished by pressure transient analysis of isochronal test (Swift et al., 1962), or evaluated from multi-rate flow test data plot coefficients (Jones et al., 1976), or type curve of single build up test following constant-rate production (Spivey et al., 2004). A simple single rate pressure transient analysis which is supported by parameters derived from historical multi rate test data was also proven to differentiate skin damage and non-Darcy skin (Aminian et al., 2007). Unfortunately there are trade-offs between accurateness and analysis time in these aforementioned methods.
Quick analysis of perforation efficiency is often needed during well completion and workover activities, to decide whether re-perforation job is required or not. To overcome the challenges of limited time for data acquisition and evaluation, an empirical relation between actual perforation length, skin damage, and laminar-turbulence flow coefficients that are obtained from short-time multi rate test is important to predict the perforation efficiency.
The empirical relation will be developed using machine learning. A simple gas reservoir model is built and then run with variations of reservoir permeability, perforation interval length, near wellbore permeability, and vertical anisotropy to generate large numbers of hypothetical multi rate test data. The data set of laminar coefficient, turbulence coefficient, absolute open flow, skin damage, and perforation length will then be trained and tested to create empirical relation using supervised regression method which will afterwards be applied to several actual field cases.
This study will elaborate the development of empirical relation of perforation efficiency with the distinct parameters obtained from simple short-time multi rate test data, what other factors will influence the empirical relation, as well as become the possible condition limit of the field application of the developed empirical relation.
Success Story of Optimizing Hydraulic Fracturing Design at Alpha Low-Permeability Reservoir
Authors: Prasetio Mario Hadinata , Anggraini Hanny , Tjahjono Hendro , Pramadana Aditya Bintang , Akbari Aulia , Madyanova Mutiara , Mekarsari Rinie , Akbarizal Andri , Setiawan Prianto 
Published: October 2021

Abstract
This paper describes the evolution of the hydraulic fracturing approach and design in the Alpha reservoir over the past years. Alpha reservoir in XYZ field is a laminated sandstone reservoir with low permeability in the range of 20 to 140 md at a depth of approximately 4,000 to 4,500 ft true vertical depth (TVD). XYZ field is located in Rokan block, Riau, Central Sumatra region.
Due to Alpha reservoir's nature, producing from this reservoir commercially requires stimulation. Hydraulic fracturing has been applied as the selected stimulation method to increase productivity from this reservoir. However, several challenges were recognized during the initial period, such as depleted reservoir pressure, indication of fracture height growth, and low to medium Young's modulus, which leads to few screened-out cases as well as low production gain after the fracturing treatment.
The fracturing job in Alpha reservoir has been applied since 2002. However, pressure depletion was observed through this time until waterflood optimization started in May 2018 by converting commingled injection to injection dedicated to the Alpha reservoir. The pressure responded and increased from 350 psi to approximately 800 psi. Hence this reservoir still cannot be produced in single completion without the hydraulic fracturing job due to laminated reservoir and low-permeability character.
A detailed look at the mechanical earth model (MEM) was done to revise the elastic properties and stress profile considering reservoir pressure change. The revised model was later used as an input for fracture geometry simulation. Calibration injection tests were performed and analyzed prior to the main fracturing treatments to determine fracture closure pressure and leakoff characteristics, which led to fracturing fluid efficiency. Results of these tests were used in job modifications regarding pad percentage, fracturing fluid rheology, proppant volume, and proppant concentration. Pressure history matching both after fracturing and in real time as well as the temperature log were used to validate the MEM and fracture geometries. Each change, approach, and impact were documented and statistically analyzed to determine a generic trend and design envelope for the Alpha reservoir.
Between 2019 and 2020, nine wells were stimulated that specifically targeted the Alpha reservoir, with continuous improvement in fracturing design and geomechanics properties with each well. After fracturing, the 30-day oil recovery was superior, higher than previous fractured wells, reaching more than 255 BFPD on average. The successful development of the Alpha reservoir with hydraulic fracturing led to further milestones to maximize oil recovery in XYZ field.
Turnaround Procedures to Ensure Personnel Safety During Covid-19: Best Practices and Case Study
Authors: Daher Elie , McCallum Shayne 
Published: October 2021

Abstract
As industry returns to work and restarts planning and execution of turnarounds and other maintenance projects, there will be regulatory, corporate and social responsibilities in order to safely restart and sustain activities. Major maintenance events and projects already bring complex challenges as multiple activities are carried out by numerous personnel and contractors, in a short duration of time. This risk is further amplified with the additional restrictions, challenges and complications posed by COVID-19.
The key challenge for turnaround managers in current times is to ensure the safety of their personnel while delivering a successful turnaround. Through a detailed analysis and breakdown of the various stages of a turnaround, this paper attempts to answer one core question- How do you keep onsite personnel safe during turnarounds in the COVID-19 era?
United Safety has developed a series of planning and prevention tools to help projects identify and implement robust actions that prevent the spread of COVID-19 onsite. These steps have been successfully implemented on site during a turnaround for a major Client in Canada. From additional precautions to new operating procedures, modifications were made at various stages including pre-planning, during the turnaround and post-turnaround stage to ensure the safety of onsite personnel.
The result was an incident-free turnaround with zero recorded transmissions on site and a comprehensive COVID-19 Turnaround Response Plan.
A Closer Look at Hydraulic Fractures: Case Studies of Microseismic Results from Horizontal Monitoring Wells
Authors: Gao Zhanwu , Li Kai , Gao Guanghui , Liao Haiyu , Zhang Yadong , Huang Zheyuan , Chen Yanyan , Liu Yuan , Li Yuanhua , Shi Ning 
Published: October 2021

Abstract
Amongst different options of hydraulic fracture geometry detection or measurement, microseismic monitoring is a commonly used method to reveal the hydraulic fracture geometry in three-dimensional space. Microseismic monitoring typically requires one or several monitoring wells within an effective range from the treatment well, in which the geophones are set to detect the microseismic events occurring during or after the treatment. In the past, most of the monitoring wells have been vertical wells. We present several recent case studies in which both the treatment and monitoring wells were horizontal wells, which produced some unique and interesting observations beyond the initial expectations.
One of the prerequisites of a proper microseismic monitoring of hydraulic fracturing treatment is to place the geophone in the proper position because a long distance between the actual fracturing events and the geophone may result in signal deterioration, which influences the processing and increases the uncertainty. This problem is more severe if the treatment well is a horizontal well because the distance from the geophone to the microseismic events varies between stages. One of the methods to solve this issue is to monitor the microseismic events in a horizontal offset well. As horizontal wells are often batched drilled in clusters for tight or unconventional resource nowadays, the availability of the monitoring well is less of a problem, and the constant distance from the monitoring well to the treatment well may help to generate better data quality and more accurate interpretation result.
We implemented horizontal well monitoring in two difference cases between 2018 and 2019. For case A, one horizontal monitoring well was used to monitor 54 fracturing stages in three offset wells, and for case B, we monitored 24 fracturing stages in one offset well. In both cases, the geophone arrays were shifted in multiple positions to fit the distance requirements, and both cases generate satisfying interpretation results.
The microseismic results from the two cases showed less uncertainty and better precision of microseismic events after processing, as we expected. What is surprising is this type of monitoring showed a unique physical phenomenon a couple of times, which is a casing background noise indicating excessive fracturing extension over a long distance. This phenomenon was captured in both cases, even with small injection rate and fluid volumes, which can be important information for us to better understand the dynamics of fracture propagation in such geomechanical environment and help to set a new guideline and design reference in the same region.
Knowledge Management Best Practices and Application in Field Development, IORs and Life-Cycle Reservoir Management
Authors: Lu Xiaoguang 
Published: October 2021

Abstract
This paper presents a unique E&P knowledge management system which has been widely accepted and applied by upstream petroleum industry. This knowledge management system started in mid-1990s and consists of standard static and dynamic knowledge base, comprehensive evaluation reports, and fit-for-purpose analytics tools applicable to the entire E&P lifecycle. Emphasis is placed on illustrating the breadth and depth of the E&P knowledge and advanced analytics in terms of their capturing and applications in field development and production.
This knowledge base consists of &gt;1600 reservoirs from around the world, each containing ~400 reservoir-level static parameters and a set of dynamic performance data. The static parameter covers reservoir characteristics, fluid properties, original in-place volume, EUR, recovery factor, production-related data (such as well spacing, well pattern, well EUR et al.), reservoir management practices, and key IORs/EORs and their incremental recovery. The knowledge extraction process involves collecting, reviewing, and synthesizing geologic, reservoir engineering and production data on a representative sample of global reservoirs. The reliable, coherent, high-quality knowledge base provides a foundation for the development of primary recovery index using supervised machine learning. Insights and intelligence derived from this knowledge base are critical to decision-making for both initial or early field development and production stages. The development application includes, but not limited to: (1) quantifying in-place volume, EUR, and recovery factor; (2) characterizing possible production performance and uncertainties and obtaining a conceptual production performance curve; (3) validating development plan options; and (4) benchmarking reservoir simulation results. The production application includes: (1) benchmarking production performance; (2) identifying upside potential and improved oil recovery opportunities; (3) finding best practices and lessons learned in reservoir management and secondary recovery practice; and (4) screening EOR methods, calibrating potential incremental recovery and characterizing EOR process performance.
Lack of knowledge standardization and absence of coherence of data from various data sources are the main challenges facing industry's data-driven application. The knowledge management system presented in this study provides the most reliable knowledge base, advanced analytics tools, and practical application workflow to help the upstream industry become more efficient in applying collective human intelligence.
A Digital Oilfield Comprehensive Study: Automated Intelligent Production Network Optimization
Authors: Naufal Aulia Ahmad , Metra Sabrina 
Published: October 2021

Abstract
Production optimization on a network level has been proven to be an effective method to maximize production potential of a field with low capital. But as it stands, it is a heavy process to start along with its several challenges such as data quality issues, tedious plus repetitive work processes to deploy and reuse a complete network model. Leveraging technologies from a flow assurance simulator, python Application Programming Interface (API) toolkit, open-source machine learning packages in python, and a commercial visualization dashboard, this paper proposed a series of workflows to simplify model deployment and set up an automatic advisory system to provide insight as a mean to justify an engineer’s day to-day engineering decision.
A total of three steps was prepared to achieve field-level automated optimization system. First, is the creation of digital twin of well and network model. To eliminate potential data errors, reduce time consumed, and to merge various part of the model into one, a scalable python script was made. Second, an automated calibration workflow is created as performance issues also arises for individual branch calibration matching. Hence a combination of technologies was utilized to automate daily data acquisition and model update from production database and run a supervised machine learning model to continuously calibrate the network model. The last one is creating the customizable optimization workflow based of field KPIs, which results are derived from daily optimization run. The results are available in a personalized network surveillance dashboard accessible for engineers to create rapid decisions.
From the first and second steps, time consumed was reduced from 30 minutes/well to 10 minutes/well in bulk well modelling workflow and from 2 hours to 10 minutes for the network model merge with the assumption of 100 wells in one network. It would also greatly increase data integrity and consistency issues as it eliminates wearisome input process. On the last step, the model was successfully updated with the latest production data and the well IPRs’ Liquid PI, reservoir pressure, and holdup factor are predicted from ML with more than 90% accuracy. As result delivery, the surveillance dashboard will be populated daily with the network production data, flowing parameters, and operation recommendations. It is estimated more than 90% time is saved from manual individual runs to digital comprehensive optimization.
Application of Machine Learning Algorithms for Managing Well Integrity in Gas Lift Wells
Authors: Ragab Adel Mohamed Salem , Yakoot Mostafa Sa’eed , Mahmoud Omar 
Published: October 2021

Abstract
Well integrity (WI) impairments in oil and gas (O&G) wells are one of the most formidable challenges in the petroleum industry. Managing WI for different groups of well services necessitates precise assessment of risk level. When WI classification and risk assessment are performed using traditional methods such as spreadsheets, failures of well barriers will result in complicated and challenging WI management, especially in mature O&G fields.
Industrial practices, then, started moving toward likelihood/ severity matrices which turned out later to be misleading in many cases due to possibility of having skewness in failure data. Developing a reliable model for classifying level of WI impairment is becoming more crucial for the industry. Artificial intelligence (AI) includes advanced algorithms that use machine learning (ML) and computing powers efficiently for predictive analytics.
The main objective of this work is to develop ML models for the detection of integrity anomalies and early recognition of well failures. Most common ML algorithms in data science include; random forest, logistic regression, quadratic discriminant analysis, and boosting techniques. This model establishment comes after initial data gathering, pre-processing, and feature engineering. These models can iterate different failure scenarios considering all barrier elements that could contribute to the WI envelope. Thousands of WI data arrays can be literally collected and fed into ML models after being processed and structured properly.
The new model presented in this paper can detect different WI anomalies and accurate analysis of failures can be achieved. This emphasizes that managing overall risks of WI failures is a robust and practical approach for direct implementation in mature fields. It also, creates additional enhancement for WI management. This perspective will improve efficiency of operations in addition to having the privilege of universality, where it can be applicable for different well groups. The rising wave of digitalization is anticipated to improve field operations, business performance, and production safety.
Artificial Intelligence Method of Flow Unit Division Based on Waveform Clustering: A Case Study on Zhetybay Oil Field, South Mangyshalk Basin, Kazakhstan
Authors: Fu Libing , Ni Jun , Liu Yuming , Li Xuanran , Xu Anzhu 
Published: October 2021

Abstract
The Zhetybay Field is located in the South Mangyshlak Sub-basin, a delta front sedimentary reservoir onshore western Kazakhstan. It was discovered in 1961 and first produced by waterflooding in 1967. After more than 50 years of waterflooding development, the reservoirs are generally in the mid-to-high waterflooded stage and oil-water distribution becomes complicated and chaotic. It is very difficult to handle and identify so much logging data by hand since the oilfield has the characteristics of high-density well pattern and contains rich logging information with more than 2000 wells. The wave clustering method is used to divide the sedimentary rhythm of the logging curve. Sedimentary microfacies manifested as a regression sequence, with four types of composite sand bodies including the composite estuary bar and distributary channel combination, the estuary bar connected to the dam edge and the distributing channel combination, the isolated estuary bar and distributing channel combination, and the isolated beach sand. In order to distinguish the flow units, the artificial intelligence algorithm-support vector machine (SVM) method is established by learning the non-linear relationship between flow unit categories and parameters based on developing flow index and reservoir quality factor, summarizing permeability logarithm and porosity degree parameters in the sedimentary facies, and analyzing the production dynamic. The flow units in Zhetybay oilfield were classified into 4 types: A, B1, B2 and B3, and the latter three are the main types. Type A is distributed in the river, type B1 is distributed in the main body of the dam, type B2 is mainly distributed in the main body of the dam, and some of B2 is distributed in the dam edge, and B3 is located in the dam edge, sheet sand and beach sand. The results show that the accuracy of flow unit division by support vector machines reaches 91.1%, which clarifies the distribution law of flow units for oilfield development. This study is one of the significant keys for locating new wells and optimizing the workovers to increase recoverable reserves. It provides an effective guidance for efficient waterflooding in this oilfield.
Probabilistic Dynamic Modelling and Prediction Workflows: Application to Multi-Layered Waterflood Reservoir with 90 Years Production History and 293 Wells
Authors: Noviyanto Ecko , Rohman Deded Abdul , Nopranda Theoza , Simanjorang Rudini , Haro Kosdar Gideon , Utomo M. Isa Priyo , Prasetyo Arif Bagus , Pratama Andi 
Published: October 2021

Abstract
This paper presents a probabilistic modeling and prediction workflow to capture the range of uncertainties and its application in a field with many wells and long history.
A static model consisting of 19 layers and 293 wells was imported as the base model. Several reservoir properties such as relative permeability, PVT, aquifer, and initial condition were analyzed to obtain the range of uncertainties. The probabilistic history matching was done using Assisted History Matching (AHM) tools and divided into experimental design and optimization. The inputted parameters and their range sensitive to objective functions, e.g., oil rate/total difference, could be determined using a Pareto chart based on Pearson Correlation during experimental design. The optimization phase carried over the most sensitive parameters and utilized Particle Swarm Optimization (PSO) algorithm to iterate the process and find the equiprobable models with minimum objective functions.
After filtering a set of models created by AHM tools by the total oil production, field/well oil objective functions, the last three years' performance, and clustering using the k-means algorithm, there are 11 models left. These models were then analyzed to understand the absolute risk and parameter uncertainties, e.g., mobile oil or sweep efficiency. Three models representing P10, P50, and P90 were picked and used as the base models for developing waterflood scenario designs. Several scenarios were done, such as base case, perfect pattern case, and existing well case. The oil incremental is in the range of 1.60 – 2.01 MMSTB for the Base Case, 7.57 – 9.14 MMSTB for the Perfect Pattern Case, and 6.01 – 7.75 MMSTB for the Existing Well Case.
This paper introduces the application of the probabilistic method for history matching and prediction. This method can engage the uncertainty of the dynamic model on the forecasted production profiles. In the end, this information could improve the quality of management decision-making in field development planning.
A New Mathematical Model of Tight Formation for Productivity Transient Analysis with Additional Gas Driving
Authors: Zhao Xiaoliang , Zhang Jiali , Liao Xinwei 
Published: October 2021

Abstract
During the gas injection development of low-permeability carbonate reservoirs, due to the complexity of the reservoir and the complexity of the fluid phases, the current productivity evaluation methods are no longer applicable. In this paper, considering factors such as fracture characteristics, stress sensitivity, and phase distribution, a low-permeability carbonate reservoir gas injection development productivity transient analysis model is established. The results of the study show that the larger the value of permeability damage coefficient, the stronger the curve of the production curve will bend toward the pressure axis. This is because the larger the value of permeability damage coefficient, the more severe the stress sensitivity of the formation is, which shows that rock deformation has an important impact on production. When two phases appear at the bottom of the well, the seepage resistance increases due to the two-phase flow, which in turn causes the productivity of a single well to rapidly decrease. With the decrease of bottom hole pressure, this resistance will increase significantly and the productivity of a single well will decline rapidly.