bluuebunny/crossref_metadata_2025_split huggingface dataset:
Oil Field Downhole Drone
Authors: Al Dabbous Mohammed , Al-Kadem Mohammad 
Published: October 2021

Abstract
The downhole drone is directed toward an autonomous Oil & Gas (O&G) utilization of a drone in subsurface wells. The autonomous submersible O&G drone comprises a circular shaped housing, a propulsion system with one or more spinner blades located at the front and rear sides driven by electromotors that can pivot along the axis of drone for locomotion determination and a control package unit: wireless transponder, power, control and data storage and sensors.
Existing technologies for surveying, such as production logging conveyed into the wellbore by coiled tubing limits wellbore access due to factors such as the length and size of wellbore, the trajectory/inclination. These factors, and in situ environmental parameters, may limit and restrict access surveying the entire wellbore via existing technologies. This adaptable for wellbore surveillance and logging includes: body, circular structure for stabilization, rechargeable spinners, digital temperature and pressure, gradiometer, gyroscope and wireless communication package. The drone is dropped into the well from the surface under shut in conditions and falls by gravity into the lowest section of the hole.
The implementation associates with minimization of man-hours with cost avoidance. The concept has a positive influence on the environment since personnel will be less exposed to hydrocarbons, especially hazardous gases such as Hydrogen Sulfide and Carbon Dioxide. The autonomous, remotely guided downhole drone surveys a predetermined area in the downhole well environment to collect well production data in terms of fluid entry/exit from the well bore, velocity as the well is in production or shut in conditions. Data such as: flow rate, temperature & pressure profiling and leak deduction. The drone moves into the production section of the well by its own propulsion as directed by the surface control system. At selected locations, it stops and takes measurements by rotating two propulsion spinners that are rechargeable; converting mechanical energy into electrical. Pressure, temperature and gradiometer information is collected continuously as the drone is propelled through the productive zone as well as when stationary. Upon completion of required data collection through the productive zone, the vehicle is simply lifted out of the hole by the flow of the well assisted by the propulsion system as required.
The future of fourth Industrial Revolution (IR 4.0) was paved commencing from the pioneering realization of the value of well intervention. This invention has a magnificent contribution in cost saving, conveyance methodologies improvement, personnel safety, energy conservation, downhole visualization and perpetuating the investment in the human capital.
A Novel Machine Learning Assisted Upscaling Workflow for Simulating the Waterflooding Process
Authors: Wang Yanji , Li Hangyu , Xu Jianchun , Fan Ling , Wang Xiaopu , Liu Shuyang 
Published: October 2021

Abstract
Conventional flow-based two-phase upscaling for simulating the waterflooding process requires the calculations of upscaled two-phase parameters for each coarse interface or block. The whole procedure can be greatly time-consuming especially for large-scale reservoir models.
To address this problem, flow-based two-phase upscaling techniques are combined with machine learning algorithms, in which the flow-based two-phase upscaling is needed only for a small fraction of coarse interfaces (or blocks), while the upscaled two-phase parameters for the rest of the coarse interfaces (or blocks) are directly provided by the machine learning algorithms instead of performing upscaling computation on each coarse interfaces (or blocks).
The new two-phase upscaling workflow was tested for generic (left to right) flow problems using a 2D large-scale model. We observed similar accuracy for results using the machine learning assisted workflow compared with the results using full flow-based upscaling. And significant speedup (nearly 70) is achieved.
The workflow developed in this work is one of the pioneering work in combining machine learning algorithm with the time-consuming flow-based two-phase upscaling method. It is a valuable addition to the existing multiscale techniques for subsurface flow simulation.
Enhancing Well, Reservoir and Facilities Management WRFM Opportunity Identification with Data Driven Techniques
Authors: Ujjwal Manu , Modi Gaurav , Simha Srungeer 
Published: October 2021

Abstract
A key to successful Well, Reservoir and Facilities Management (WRFM) is to have an up-to-date opportunity funnel. In large mature fields, WRFM opportunity identification is heavily dependent on effective exploitation of measured & interpreted data. This paper presents a suite of data driven workflows, collectively called WRFM Opportunity Finder (WOF), that generates ranked list of opportunities across the WRFM opportunity spectrum.
The WOF was developed for a mature waterflooded asset with over 500 active wells and over 30 years of production history. The first step included data collection and cleanup using python routines and its integration into an interactive visualization dashboard. The WOF used this data to generate ranked list of following opportunity types: (a) Bean-up/bean-down candidates (b) Watershut-off candidates (c) Add-perf candidates (d) PLT/ILT data gathering candidates, and (e) well stimulation candidates. The WOF algorithms, implemented using python, largely comprised of rule-based workflows with occasional use of machine learning in intermediate steps.
In a large mature asset, field/reservoir/well reviews are typically conducted area by area or reservoir by reservoir and is therefore a slow process. It is challenging to have an updated holistic overview of opportunities across the field which can allow prioritization of optimal opportunities. Though the opportunity screening logic may be linked to clear physics-based rules, its maturation is often difficult as it requires processing and integration of large volumes of multi-disciplinary data through laborious manual review processes. The WOF addressed these issues by leveraging data processing algorithms that gathered data directly from databases and applied customized data processing routines. This led to reduction in data preparation and integration time by 90%. The WOF used workflows linked to petroleum engineering principles to arrive at ranked lists of opportunities with a potential to add 1-2% increment in oil production. The integrated visualization dashboard allowed quick and transparent validation of the identified opportunities and their ranking basis using a variety of independent checks. The results from WOF will inform a range of business delivery elements such as workover & data gathering plan, exception-based-surveillance and facilities debottlenecking plan.
WOF exploits the best of both worlds - physics-based solutions and data driven techniques. It offers transparent logic which are scalable and replicable to a variety of settings and hence has an edge over pure machine learning approaches. The WOF accelerates identification of low capex/no-capex opportunities using existing data. It promotes maximization of returns on already made investments and hence lends resilience to business in the low oil price environment.
Best Practices in Development and Production Optimization of Offshore Low Permeability Reservoir
Authors: Zhang Lijian , Tian Panpan , Xing Chuanheng , Bai Ruiting , Yin Yanjun , Li Quan , Zhu Jianying , Zhang Qiang 
Published: October 2021

Abstract
The development effect of low permeability reservoirs of offshore Shahejie Formation in China is poor with fast formation pressure drop, rapid production decline and numerous abnormal wells due to the influence of low permeability, strong heterogeneity, ultra-high reservoir temperature and pressure and high wax and asphaltene content. To overcome these difficulties, this paper takes geological conditions, reservoir potential, stimulation technology and actual wells situation into consideration on the basis of the reservoir performance research, and gives full play to the integration of reservoir and mechanical recovery. A set of production enhancement and efficiency improvement technologies has been formed, including moderate periodic water injection based on pressure fluctuation theory, liquid extraction optimization for low permeability fields, modified compound plugging removal technology, small flowrate and broad width electric pump selection technology, and pump stuck early warning technique to achieve the efficient development of offshore low-permeability oilfields. Moderate periodic water injection not only makes full use of the imbibition effect to achieve the equilibrium displacement, but also avoids the water channeling in fracture area; eight main controlling factors are used to classify and evaluate the liquid extraction potential and select the well group at level-1 for the workover with the effect of daily oil increase of 42 m3; modified compound plugging removal technique completely removes the pollution and plugging of the formation and the wellbore to release the potential of the oil wells to the greatest extent and cumulative oil increase of 6 well times is expected to be 12.44×104 m3; small flowrate and broad width electric pump selection technology applied for more than 30 well times improves the adaptability of the pumps substantially and extends the pump inspection cycle to 857 days; real-time pump stuck warning technique through the dual pattern of analyzing both conventional and high-frequency data guarantees the efficient operation of oil wells. The technologies in this paper have been applied in the target low-permeability field with daily oil production of 429 m3, oil recovery rate of 1.1%, natural decline rate drop year by year and the expected recovery factor of 20.9% under current well pattern. The key technologies in this paper have improved the target reservoir development effect significantly and provided important reference significance for other offshore low-permeability reservoirs development.
Swing Operation Envelope of River-Sourced Water Treatment Facility for Water Injection Purpose in Banyu Urip Field
Authors: Rois Muhammad Amin , Dharmawan Willy 
Published: October 2021

Abstract
Banyu Urip reservoir management heavily rely on river-sourced water as water injection to meet Voidage Replacement Ratio target of 1. The treatment facility which consist of Raw Water Basin, Clarifiers, Multi Media Fine (MMF) Filters and Cartridge Filters, is sensitive to seasonal transition and river condition. This paper shares lesson learnt in operating such facility and troubleshooting guidance to overcome challenges of high turbidity during rainy season and lack of river water volume during drought season.
To maintain the design intent of Banyu Urip (BU) water treatment facility in achieving water injection quality and quantity at reasonable cost, following activities were undertaken: [1] Critical water parameters data gathering & analysis across each unit; [2] Clarifier Chemical injection dosage verification based on laboratory test; [3] MMF Media coring inspection to assess the filtering media condition; [4] MMF Filters backwash parameters optimization; [5] MMF Filter on-off valve sequencing optimization to address water hammering issue; [6] Water injection rate management to deal with river water source availability along the year.
Critical water parameters analysis revealed that chemical dosages were in-adequate to treat the five times higher turbidity coming into Clarifiers during early rain 2019. On top of this, low Raw Water Basin level at the end of long drought further contributed to jeopardize Clarifier's operation. Although in-adequate chemicals injection was resolved at early 2020, the treatment cost remained high, especially on filtration section. Media coring result on MMF Filters confirmed that the filtering media have been poisoned by carried-over mud from Clarifiers during upset. The operation of MMF Filters required extensive optimization on backwash parameters to successfully recover the MMF Filters performance without media replacement. Latest media coring on the worst MMF Filter showed that there was no more top mud layer and the amount of trapped mud had been decreased significantly. Cartridge Filter replacement interval was improved from 38 hours to 186 hours, therefore water treatment cost dropped with quite significant margin. Additionally, the availability of each MMF Filters was also improved. At the same time, the high water injection rate during 2020 rainy season, had successfully increased reservoir pressure buffer up to its maximum point as the anticipation of prolonged drought season.
This paper provides the troubleshooting guidance for MMF Filter application in season-prone water treatment facility including insights on interpretation of media coring result and linking it back to optimization strategy on the MMF Filters drain down time for effective backwash process without having excessive media loss.
Successfully Learning from Failures: A Mahakam Legacy
Authors: Kusumawati Diah , Puspa Winda Wiria , Kurniawan Nandi Yanuar 
Published: October 2021

Abstract

Summary
Incidents can incur damage on people, environment, asset and even on Company's reputation. Therefore, effective learning from incidents plays a critical role in Company's HSE management system. Effective learning from incidents can also be one of predictors of an effective HSE management system and positive safety culture (Reason, 2016). This study was undertaken to discuss Mahakam's (now operated by PT. X) experience in the implementation of learning from incidents and its contribution to improve Company's safety performance. Learning cycle from Jacobsson is used as main reference to assess the effectiveness of learning from incidents system in the company (Jacobsson et al., 2011).
Establishing & Estimating Gas-Liquid Performance Characteristics of Multiphase Pumps
Authors: Ejim Chidirim Enoch 
Published: October 2021

Abstract
Multiphase electric submersible pumps (ESPs) are used to produce gas and liquid in wells with high gas content. These pumps are operated at different speeds, and designed to handle flows with various gas volume fractions (GVFs). This study uses gas-liquid dimensionless parameters to obtain and compare the performance of conventional multiphase pumps. Knowledge of such techniques is important for production engineers, field operators and application engineers to ascertain pump performance for given gas-liquid operating conditions.
Gas-liquid performance data for two multiphase pumps with 8.00-inch and 8.62-inch housing diameters were obtained from open literature. The inlet pressure, GVF and rotational speed ranges were 100 to 300 psig, 0 to 0.57, and 3000 to 3600 revolutions per minute (RPM), respectively. The total flow rates varied from 15000 to 60000 barrels per day (BPD). Euler turbomachinery principles for gas-liquid flows were applied to the data to obtain required dimensionless parameters and two-phase dimensionless performance curves for the pumps. The method was tested using dimensionless curves for a given operating condition to obtain pump performance at another operating condition.
The results showed that for each rotational speed, the difference in dimensionless pressure between the multiphase pump discharge and inlet decreased with increasing mass-quality-weighted volume flow rate. For each weighted volume flow rate, the difference in pump discharge and inlet dimensionless pressures decreased with increasing intake GVF. The decrease with increasing intake GVF can range between a factor of 3 and 4, depending on the magnitude of the weighted volume flow rate. Using the 3000 RPM data, a two-phase (gas-liquid) dimensionless performance curve was obtained for one of the multiphase pumps with intake GVF and dimensionless volume flow rate parameter as the independent variables. The curve was used to estimate pump performance at 3600 RPM and then compared with the actual reference test data. For the second multiphase pump, two datasets at different intake pressures were used to obtain the effects of intake pressure. The performance for this multiphase pump was a function of dimensionless volume flow rate, intake GVF and intake gas-liquid density ratio. The maximum error in the estimated performance data was within 7%. Overall, the performance of multiphase pumps can be estimated using the technique in this study for the flow conditions analyzed.
This study highlights the importance of obtaining dimensionless two-phase performance characteristics of multiphase pumps. Given that these pumps are frequently used in oilfield production operations, capability to determine the pressure boosting performance of the pumps, for given operating conditions, is important to field operating personnel and design engineers. This knowledge benefits the operator to optimally produce hydrocarbons from high gas-content wells and maximize the economic bottom line from the field asset.
The Art of Deploying Data Mining and Machine Learning in Developing and Managing Deepwater Turbidite Gas Assets
Authors: Pratama Edo 
Published: October 2021

Abstract
Many oil and gas operators have challenges in deepwater turbidite gas asset's reservoir management plan (RMP) readiness due to lack of experience and very limited analog field data. The objective of this article is to demonstrate how data analytics workflow, comprising of data mining and machine learning-based global deepwater turbidite gas field benchmarking and lessons learned, to identify field performance and mitigate subsurface challenges in developing and managing deepwater turbidite gas assets.
To mine turbidite field data from around the world, a customized R script was constructed using optical character recognition, regular expression (regex), rule-based logic to extract subsurface and surface data attributes from unstructured data sources. All extracted contents were transformed into a properly structured query language (SQL) database relational format for the cleansing process. Having established the turbidite assets repository, exploratory data analysis (EDA) was then employed to discover insight datasets. To analyze the field performance, the number of wells needed to deplete the field was identified using support vector regression, subsequently, K-means clustering was used to classify the reservoirs productivity.
The results of field benchmarking analysis from EDA are deployed in a fit-for-purpose dashboard application, which provides an elegant and powerful framework for data management and analytics purposes. The analytic dashboard which was developed to visualize EDA findings will be presented in this article. The productivity of deepwater turbidite gas reservoirs has been classified based on the maximum gas flow rate and estimated ultimate recovery per well. This result help in identifying the high-rate, high-ultimate-recovery (HRHU) reservoirs of a deepwater turbidite gas field. The regex pattern for subsurface challenges specifically as related to reservoir uncertainties and associated risks, including operational challenges in developing and managing deepwater turbidite gas fields were identified through word cloud recognition. Key subsurface challenges were then categorized and statistically ranked, finally, a decomposition tree was used to identify the issues, impacts, and mitigation plan for dealing with identified risks based on best practices from a global project point of view.
Deployment of this novel workflow provides insight for better decision-making and can be a prudent complementary tool for de-risking subsurface uncertainties in developing and managing deepwater turbidite gas assets. The findings from this study can be used to develop the framework that captures current best-practices in the formulation and execution of a RMP including monitoring and benchmark of asset performance in deepwater turbidite gas fields.
The Development and Field Applications of Ultra-Deepwater Structural Conductor Jetting in Western South China Sea
Authors: Li Zhong 
Published: October 2021

Abstract
Lingshui X-1 block is located in ultra-deepwater region in western South China Sea. Drilling in this area are encountering many technical problems, such as low temperature, poor lithology in shallow formation, low fracture pressure gradient, gas hydrate and shallow geological hazards, which bring great technical challenges to subsea wellhead stability (Yang et al., 2013). In order to ensure wellhead stability and improve top-hole operation efficiency, jetting technology was used for spud-in. First of all, carrying capacity curve of structural conductor was obtained from mechanics analysis of shallow seabed soil in Lingshui X-1 block. Secondly, structural conductor size selection and load analysis were carried out to determine safe setting depth of structural conductor in Lingshui X-1 block. Finally, bit stick-out, bit size selection, Weight on Bit (WOB) and pump rate were optimized on the basis of comprehensive analysis of ultra-deepwater under top-hole jetting technology and BHA characteristics. Well LSX-1-1 was taken as an example to illustrate field operation for top-hole jetting. This successful case of top-hole jetting technology in Lingshui X-1 block of western South China Sea could provide technical guidance for future drilling activity in similar ultra-deepwater wells.
Unravel Carbonate Reservoir Heterogeneities Through 3D Seismic Neural Network Application: A Machine Learning Based Approach and Workflow
Authors: Ramadhan Dimmas , Laya Krishna Pratama , Rizkiaputra Ricko , Sinlae Esterlinda , Subekti Ari , Iswara Anindyajati Anak Agung Gde 
Published: October 2021

Abstract
The availability of 3D seismic data undoubtedly plays an important role in reservoir characterization. Currently seismic technology continues to advance at a rapid pace not only in the acquisition but also in processing and interpretation domain. The advance on this is well supported by the digitalization era which urges everything to run reliably fast, effective and efficient. Thanks to continuous development of IT peripherals we now have luxury to process and handle big data through the application of machine learning. Some debates on the effectiveness and threats that this process may automating certain task and later will decrease human workforce are still going on in many forums but still like it or not this machine learning is already embraced in almost every aspect of our life including in oil & gas industry.
Carbonate reservoir on the other hand has been long known for its uniqueness compared to siliciclastic reservoir. The term heterogeneous properties are quite common for carbonate due to its complex multi-story depositional and diagenetic facies. In this paper, we bring up our case where we try to unravel carbonate heterogeneity from a massive tight gas reservoir through our machine learning application using the workflow of supervised and unsupervised neural network. In this study, we incorporate 3D PSTM seismic data and its stratigraphic interpretation coupled with the core study result, BHI (borehole image) log interpretation, and our regional understanding of the area to develop a meaningful carbonate facies model through seismic neural network exercises. As the result, we successfully derive geological consistent carbonate facies classification and distribution honoring all the supporting data above though the limitation of well penetration in the area. This result then proved to be beneficial to build integrated 3D geomodel which later can explain the issue on different gas compositions happens in the area. The result on unsupervised neural network also able to serves as a quick look for further sweetspot analysis to support full-field development.