bluuebunny/crossref_metadata_2025_split huggingface dataset:
CoalBed Methane Pad Wells Completion and Artificial Lift Optimizations: Case Study From Australia Surat Basin DS Gas Field
Authors: Ming Li , Zhaohui Xia , Lingli Liu , Zehong Cui , LiJiang Duan , Jianjun Wang 
Published: March 2021

Abstract
The Coalbed Methane in Australia Surat Basin is main gas source for LNG project in east coast of Australia[1]. Traditionally, Coalbed Methane are drilled by vertical wells. But there are big intensively farmed land coverage in the Surat Basin, the multiple wells on single, larger pad from the surface, the bores slant away at around 70 degrees to intersect multiple, thin coal seams are applied to avoid the extra "footprint" and decrease the environment affect. Many pad wells production failure because of poor interburden isolation. Excessive solids production in pad wells resulted in new failures of holes in tubing due to accelerated erosion, which bring big challenges for the Coalbed methane production in deviated pad wells.
The gas production in pad wells are analysised and the new pad wells optimization are proposed. First, the complete wire log (at least include GR and density log curves) need to acquire for correct thin coal seams correlation and locate the interburden sandstone position for future good sandstone isolation. Second the customized completion strategy and placement (swell packer) are applied in the pad wells and specialized tubing with enhanced erosion resistance to extend the run life. Thirdly ESP pumps and optimized tubing are installed in new deviated wells for good gas production.
After the pad wells were put into production, the gas production was kept well for long time without pump problems. Swell able packer completion significantly eliminates sand problem by isolating in excess of or close to 80% of interburden sand.
The above well completion and artificial lift optimization methods bring good production performance for the new pad wells and contribute much production for the producing gas field. The swellable packer completion also can be used in vertical wells and will be standard well completion methods for future gas development wells.
Hybrid Physics-Field Data Approach Improves Prediction of ROP / Drilling Performance of Sharp and Worn PDC Bits
Authors: Zhan Guodong David , Magana-Mora Arturo , Moellendick Eric , Bomidi John , Huang Xu , Bird Marc 
Published: March 2021

Abstract
This study presents a hybrid approach that combines data-driven and physics models for worn and sharp drilling simulation of polycrystalline diamond compact (PDC) bit designs and field learning from limited downhole drilling data, worn state measurements, formation properties, and operating environment. The physics models include a drilling response model for cutting forces, worn or rubbing elements in the bit design. Decades of pressurized drilling and cutting experiments validated these models and constrained the physical behaviour while some coefficients are open for field model learning. This hybrid approach of drilling physics with data learning extends the laboratory results to application in the field. The field learning process included selecting runs in a well for which rock properties model was built. Downhole drilling measurements, known sharp bit design, and measured wear geometry were used for verification. The models derived from this collaborative study resulted in improved worn bit drilling response understanding, and quantitative prediction models, which are foundational frameworks for drilling and economics optimization.
The Dusk of MEFS in the Digital Era of Exploration Value Creation
Authors: Surovtsev Dmitry , Joshi Parth , Sethi Muhammad Usman 
Published: March 2021

AbstractThe concept of minimum economic field size (MEFS) has been used by explorationists for almost four decades. MEFS is often the only filter to distinguish between a commercial and a non-commercial discovery—far before a wildcat well is drilled—to test a prospect for a working petroleum systems hypothesis. As simple as it gets, the concept started to lose traction in the 21st century as subsurface targets became more and more challenging. In the case of tight hydrocarbons, it is fairly common to observe a P90 case net present value (NPV) to be negative, a P50 case to be positive, and a P10 case to be negative again. The reason for this outcome is that a whole set of full-cycle factors, in addition to the field size, affects prospect commerciality. Their uncertainty ranges can match or exceed resource estimate uncertainty. These factors include, but are not limited to, initial productivity of development wells, estimated eltimate recovery (EUR) per well, decline curve parameters, capital investments, operating costs, and the project phases’ durations.A new way of handling the full universe of risks and uncertainties faced by modern explorers is already available in the new generation of industry-leading integrated prospect risk, resource and value assessment software. Innovators and thought leaders can already substitute MEFS with a commerciality threshold (CT) that neatly mimics board considerations at the final investment decision (FID) stage gate. Others can consider the economic chance of success (ECOS) estimated with a probabilistic full-cycle mindset, as an additional metric valuable for risk management purposes.Using fictional case studies inspired by real-life assessment situations, we discuss the additional value creation by a CT-powered workflow as compared to an MEFS-based one and explain the reasons for the key differences.The discussed workflow does not eliminate nature-specific uncertainties; neither does it reduce the geological risk. However, it helps to better understand human-controlled risks and prepare management exploration decisions with a greater degree of confidence.
TitleApplication of M4C P-wave and S-wave Joint Inversion in Shallow Reservoir Identification
Authors: Zhu Sian , Chen Hongtao , Hu Yongjun , Yang Feng , Feng Yubin 
Published: March 2021

Abstract
The genetic mechanism of shallow gas reservoirs is complex, which is usually characterized by shallow burial depth, multiple types, low reserves and wide distribution, so that the inversion based on P-wave data alone may be ambiguous. For shallow reservoir with large lateral variation, it is hard to accurately predict oil and water distribution by conventional P-wave prestack inversion. Marine four-component (M4C) P-wave and S-wave joint inversion can solve the problem effectively. M4C seismic survey collects P-wave and S-wave seismic data. An initial model can be established based on fine structural interpretation of P-wave and S-wave data and S-wave compression pattern matching. It lays a good foundation for subsequent P-wave and S-wave joint inversion. Based on the P-wave seismic record equation proposed by Fatti et al., a seismic record equation from poststack P-wave and S-wave joint inversion was derived according to the relationship among reflection coefficient, P-wave impedance, S-wave impedance and density, then important lithologic parameters (P-wave impedance, S-wave impedance and density) were calculated, and finally the ratio of P-wave velocity to S-wave velocity which is more sensitive to oil and gas was obtained. According to the ratio of P-wave velocity to S-wave velocity, the oil and gas distribution was predicted in shallow Bohai Bay Basin. Application has proven that the inversion can well reflect the fluid distribution, and the coincidence between the inversion results and the drilling data is up to 85%.M4C seismic survey was conducted for the first time to the shallow oil and gas reservoirs with rapid lateral variation in the Bohai Bay Basin, and collected raw P-wave and S-wave seismic data. Based on the data, the precision and reliability of P-wave and S-wave joint inversion was improved. The results provided strong technical support to the reserves and production increase in the Bohai Bay Basin.
Jumping on the Digitalization Bandwagon for BOP Pressure Testing
Authors: Lai Kheng Looi , Provan Martin , Abdullah Malik Faisal , Hoak Eric , Hoke Gabe 
Published: March 2021

Abstract
Blowout Preventer (BOP) is mainly used to control well pressure by quick well shut in the event of overflow and well kick to prevent blowout on the rigs during drilling, completion, workover, and plug and abandonment phases of well operations. Regulators, Operators and Drilling contractors have put in place the requirement to test BOP systems as a method of inspection and assurance in this process safety critical steps. During well operations regular BOP pressure testing will need to be conducted to ensure its integrity and functionality as per testing requirement. In most cases BOP pressure testing is conducted online using rig time although it can also be conducted offline in some circumstances.
BOP Pressure testing is considered flat time during well operations and the operators’ goal is to minimize flat times for rig time saving thus operating cost reduction. Flat time reduction can be achieved by reducing BOP pressure testing period and improving the efficiency in the entire testing process. As such a digital pressure testing system was deployed to multiple offshore drilling rigs in Malaysia beginning in September 2019 as innovative technological solutions.
This paper represents the digital pressure testing system deployment study on both subsea and surface BOP drilling rigs for direct comparison with the process in use of the analog circular charter recorders (CCR) for BOP Pressure Testing. The study has shown an average 22% reduction in test times, improved safety, improved efficiency in recognizing failed tests faster, improved data reliability and repeatability of BOP pressure tests.
FEP Based Model Development for Assessing Well Integrity Risk Related to CO2 Storage in Central Luconia Gas Fields in Sarawak
Authors: Patil Parimal A. , Chidambaram Prasanna , Bin Ebining Amir M Syafeeq , Tiwari Pankaj K. , Das Debasis P. , Picha Mahesh S. , B A Hamid M Khaidhir , Tewari Raj Deo 
Published: March 2021

AbstractUnderground storage of CO2 in depleted gas reservoirs is a greenhouse gas reduction technique that significantly reduces CO2 released into the atmosphere. Three major depleted gas reservoirs in Central Luconia gas field, located offshore Sarawak, possess good geological characteristics needed to ensure long-term security for CO2 stored deep underground. Long-term integrity of all the wells drilled in these gas fields must be ensured in order to successfully keep the CO2 stored for decades/centuries into the future. Well integrity is often defined as the ability to contain fluids without significant leakage through the project lifecycle.In order to analyze the risk associated with all 38 drilled wells, that includes 11 plugged and abandoned (P&A) wells and 27 active wells, probabilistic risk assessment approach has been developed. This approach uses various leakage scenarios, that includes features, events, and processes (FEP). A P&A well in a depleted reservoir is a very complex system in order to assess the loss of containment as several scenarios and parameters associated to those scenarios are difficult to estimate. Based on the available data of P&A wells, a well has been selected for this study. All the barriers in the example well have been identified and properties associated with those barriers are defined in order to estimate the possible leakage pathways through the identified barriers within that well. Detailed mathematical models are provided for estimating CO2 leakage from reservoir to the surface through all possible leakage pathways.Sensitivity analysis has been carried out for critical parameters such as cement permeability, and length of cement plug, in order to assess the containment ability of that well and understand its impact on overall well integrity. Sensitivity analysis shows that permeability of the cement in the annulus, and length of cement plug in the wellbore along with pressure differential can be used as critical set of parameters to assess the risk associated with all wells in these three fields.Well integrity is defined as the ability of the composite system (cemented casings string) in the well to contain fluids without significant leakage from underground reservoir up to surface. It has been recognized as a key performance factor determining the viability of any CCS project. This is the first attempt in assessing Well Integrity risk related to CO2 storage in Central Luconia Gas Fields in Sarawak. The wells have been looked individually in order to make sure that integrity is maintained, and CO2 is contained underground for years to come.
Real-Time Quantification of Offshore Oil Spills During Blow-Out Accident
Authors: Chandra Steven , Sajjad Farasdaq 
Published: March 2021

Abstract
In the event of offshore oilfield blow-out, real-time quantification of both spilled volume, recovered oil and environmental damage is essential. It is due to costly recovery and restoration process. In order to develop a robust and accurate quantification, we need to consider numerous parameters, which are sometimes tricky to be identified and captured. In this work, we present a new modeling technique under uncertainty, which accommodates numerous parameters and interaction among them.
We begin the model by identifying possible parameters that contributes to the process: grouped into (1) subsurface, (2) surface and (3) operations. Subsurface e.g. well and reservoir characteristics. Surface e.g. ocean, wind, soil. (3) Operations e.g. oil spill treatment blow-out rate, oil characteristics, reservoir characteristics, ocean current speed, meteorological aspects, soil properties, and oil-spill treatment (oil booms and skimmers). We assign prior distribution for each parameter based on available data to capture the uncertainties.
Before progressing to uncertainty propagation, we construct objective response (amount of recovered oil) through mass conservation equation in data-driven and non-intrusive way, using design of experiment and regression-based method. We propagate uncertainties using Monte Carlo simulation approach, where the result is presented in a distribution form, summarized by P10, P50, and P90 values.
This work shows how to robustly calculate the amount of recovered oil under uncertainty in the event of offshore blow out. There are several notable challenges within the approach: 1) determining the uncertainty range in blow-out rate in case of rupture occurs in the well, 2) obtaining data for wind and ocean current speed since there is an interplay between local and global climate, and 3) accuracy of capturing the shoreline geometry. Despite the challenges, the results are in-line with the physics and several recorded blow-out cases.
Through sensitivity analysis with Sobol decomposition, we can define the heavy hitters. These heavy hitters give us knowledge on which parameters should be aware of. In real-time quantification, this analysis can provide an insight on what treatment method should be performed to efficiently recover the spill. We also highlight about the sufficiency of the model to obtain several parameters’ range, for example blow-out rate. The model should at least capture the physics in high details and incorporate multiple scenarios. In the case of blow-out rate, we extensively model the well completion and consider leaking due to unprecedented fractures or crater formation around the wellbore.
We introduce a new framework of modeling to perform real-time quantification of offshore oil spills. This framework allows inferring the causality of the process and illustrating the risk level.
Predictive Modelling of CO2 Injectivity Impairment due to Salt Precipitation and Fines Migration During Sequestration
Authors: Md Yusof Muhammad Aslam , Ibrahim Mohamad Arif , Mohamed Muhammad Azfar , Md Akhir Nur Asyraf , M Saaid Ismail , Ziaudin Ahamed Muhammad Nabil , Idris Ahmad Kamal , Awangku Matali Awangku Alizul 
Published: March 2021

Abstract
Recent studies indicated that reactive interactions between carbon dioxide (CO2), brine, and rock during CO2 sequestration can cause salt precipitation and fines migration. These mechanisms can severely impair the permeability of sandstone which directly affect the injectivity of supercritical CO2 (scCO2). Previous CO2 injectivity change models are ascribed by porosity change due to salt precipitation without considering the alteration contributed by the migration of particles. Therefore, this paper presents the application of response surface methodology to predict the CO2 injectivity change resulting from the combination of salt precipitation and fines migration. The impacts of independent and combined interactions between CO2, brine, and rock parameters were also evaluated by injecting scCO2 into brine saturated sandstone. The core samples were saturated with NaCl brine with salinity between 6,000 ppm to 100,000 ppm. The 0.1, 0.3, and 0.5 wt.% of different-sized hydrophilic silicon dioxide particles (0.005, 0.015, and 0.060 μm) were added to evaluate the effect of fines migration on CO2 injectivity alteration. The pressure drop profiles were recorded throughout the injection process and the CO2 injectivity alteration was represented by the ratio between the initial and final injectivity. The experimental results showed that brine salinity has a greater individual influence on permeability reduction as compared to the influence of particles (jamming ratio and particle concentration) and scCO2 injection flow rate. Moreover, the presence of both fines migration and salt precipitation during CO2 injection was also found to intensify the permeability reduction by 10%, and reaching up to threefold with increasing brine salinity and particle size. The most significant reductions in permeability were observed at higher brine salinities, as more salts are being precipitated out which, in turn, reduces the available pore spaces and leads to a higher jamming ratio. Thus, more particles were blocked and plugged especially at the slimmer pore throats. Based on comprehensive 45 core flooding experimental data, the newly developed model was able to capture a precise correlation between four input variables (brine salinity, injection flow rate, jamming ratio, and particle concentration) and CO2 injectivity changes. The relationship was also statistically validated with reported data from five case studies.
Machine Learning as Accelerating Tool in Remote Operation Realisation through Monitoring Oil and Gas Equipments and Identifying its Failure Mode
Authors: Naufal Ahmad Naufal , Samy Samy Abdelhamid , Nenisurya Nenisurya Hashim , Zaharuddin Zaharuddin Muhammad , Eddy Eddy Damsuri , Amir Amir Ali , Hilmi Mohd Hilmi , Izzatdin Izzatdin A , Jafreezal Jafreezal Jaafar , Norshakirah Norshakirah A , Emelia Emelia Akashah , Amirul Ku Amirul , Hajar Hajar M , Wahyu Ade Wahyu , Syakirah Nur Syakirah 
Published: March 2021

Abstract
Equipment failure, unplanned downtime operation, and environmental damage cost represent critical challenges in overall oil and gas business from well reservoir identification and drilling strategy to production and processing. Identifying and managing the risks around assets that could fail and cause redundant and expensive downtime are the core of plant reliability in oil and gas industry. In the current digital era; there is an essential need of innovative data-driven solutions to address these challenges, especially, monitoring and diagnosis of plant equipment operations, recognize equipment failure; avoid unplanned downtime; repair costs and potential environmental damage; maintaining reliable production, and identifying equipment failures. Machine learning-artificial intelligence application is being studied to develop predictive maintenance (PdM) models as innovative analytics solution based on real-data streaming to get to an elevated level of situational intelligence to guide actions and provide early warnings of impending asset failure that previously remained undetected.
This paper proposes novel machine learning predictive models based on extreme learning/support vector machines (ELM-SVM) to predict the time to failure (TTF) and when a plant equipment(s) will fail; so maintenance can be planned well ahead of time to minimize disruption. Proper visualization with deep-insights (training and validation) processes of the available mountains of historian and real-time data are carried out.
Comparative studies of ELM-SVM techniques versus the most common physical-statistical regression techniques using available rotating equipment-compressors and time-failure mode data. Results are presented and it is promising to show that the new machine learning (ELM-SVM) techniques outperforms physical-statistics techniques with reliable and high accurate predictions; which have a high impact on the future ROI of oil and gas industry.
Application of Fracture Prediction Research Based on OVT Gather in Granite Buried Hills
Authors: Sun Weizhao , Wang Zhongfan , Xia Yaliang , Chen Gang , Zhang Suhong 
Published: March 2021

Abstract
The space of fracture reservoir in granite buried hills varies sharply with strong heterogeneity and very difficult to predict. Conventional fracture prediction methods based on narrow-azimuth post-stack seismic data have low prediction accuracy. OVT pre-stack gather retains the information of offset and azimuth, reflects the anisotropic characteristics of fractures and can be effectively used to predict the strength and strike of fracture in granite buried hills. In this paper, based on the analysis of anisotropy of OVT gather data, according to the geological characteristics of granite fractures, optimizes the sensitive offset and azimuth OVT gather data, uses the P-wave azimuth anisotropy technique to predict the strength and strike of fractures. The prediction coincides well with the drilling results, which can provide an important guidance for the exploration and development of granite buried hill in the research area.