bluuebunny/crossref_metadata_2025_split huggingface dataset:
Innovative Pressure Surge and Water Hammer Mitigation Control Methodology in Upstream Liquid Pipelines
Authors: Alshehri Ali , Salu Samusideen , Soliman Mohamad 
Published: February 2023

Abstract
This paper is to introduce the newly developed Anti-Surge valve and control methodology that is capable of mitigating and reducing the magnitude of pressure surges or water hammer phenomenon in liquid pipeline system. The innovative anti-surge valve is a full-bore valve with two circular/concave orifice plates, which can rotate 90 degree in the axial or horizontal interval based on the surge controller indication. Once the surge pressure is being generated in the pipeline, the two circular/concave orifice plates will be in a position, which are particularly able to dissipate the generated pressure waves and decelerate the fluid velocity which as a result can mitigate and reduce the magnitude of pressure surges in the pipeline. By dissipating the generated pressure surge and decelerating the high fluid velocity, the newly developed anti-surge valve is able to protect the pipeline safely and adequately with no external equipment for fluid release. Thus, it can effectively minimize the OPEX and CAPEX for the surge protection system in the upstream oil production facilities or pipelines. The device can enhance the surge protection system by self-dependence, which does not require any external source to be used for equipment operation compared to convention surge protection system using Nitrogen. As a result, it can improve the reliability and efficiency of the surge protection system. This innovative anti-surge valve and control have been officially granted as a new patent in the US Patent and Trademark Office (USPTO) under Patent No. 11,209,842. The new surge control methodology will eliminate water hammering and potential pipeline damages without the use of external equipment to mitigate the surge.
Key Considerations for Screening and Selection of Saline Aquifers for CO2 Storage
Authors: Alarfaj Malik , Vidal Faez Angelo 
Published: February 2023

AbstractCarbon capture and storage (CCS) presents a key solution to reducing emissions especially from stationary power and industrial sites. A major component of CCS is CO2 storage in geologic formations, including saline aquifers, presents a great opportunity for the oil and gas industry to capitalize on their subsurface expertise to ensure that CO2 is stored safely underground over the long term. Saline aquifers are particularly relevant in the Middle East, where hydrocarbon reservoirs may not be at high depletion stages and so may not be available for CO2 storage yet. This paper looks at key geologic formation considerations related to CO2 storage in saline aquifers, highlighting the static and dynamic characteristics of the rock and the fluids that determine how much CO2 can be stored and at what injection rate. It also highlights how these characteristics affect the economics of the subsurface component of a CCS project.This paper shall provide an overview of the key aspects that guide subsurface storage evaluations: containment ability, trapping mechanisms, storage capacity, and injectivity. It will highlight the different storage capacity assessment methods. Then it shall discuss resource classification and categorization, and determination of commerciality as per the SPE CO2 Storage Resources Management System (SRMS). Finally, the paper goes into further considerations that need to be taken into account beyond the initial screening, including the data acquisition program and the impact of CO2 stream impurities on storage potential, geologic formation properties and cap rock integrity.The work illustrates the importance of understanding the physics of CO2 injection into a water-bearing system and lays out considerations for screening potential saline aquifer sites from a reservoir engineering perspective. The screening process should evaluate the anticipated CO2 trapping mechanism, assure its ability to contain the injected fluids over the long term, and estimate its storage capacity and injectivity.
Deliberate Search for Stratigraphic Traps: A Success Story from Sabah Offshore
Authors: Das Prabal S , Nayak Satyabrata , Kurniawan Trisakti , Huslan B Mohd Azwari 
Published: February 2023

Extended AbstractAbstractThis article briefly discusses the workflow through which a gas discovery was made within the Late Miocene interval (Lower and Upper Stage IVD) from the structurally down-flank of a three-way fault closure, where previously an unsuccessful campaign was carried out in the structurally higher location. The causes for the failure were attributed to reservoir absence and trap incompetency. An attempt was made to understand the causes of facies variations and their limits through an integrated sequence stratigraphic approach. This model was further concretized through post-stack attributes where the limits of the seismic facies were prominent. A quantitative interpretation (QI) study coupled with forward modelling helped de-risk the reservoir presence and fluid types. Rock physics modelling work, including shear log prediction, rock property modelling, depth -trend analysis, followed by simultaneous inversion and sand probability volume generation, reveals that the deeper part of Upper Stage IVD and Lower Stage IVD intervals were shale-out and pinch-out, respectively, for the earlier campaign. Likewise, sand-dominated facies are likely at the down-dip for both intervals with an effective lateral seal up-dip (due to facies change and pinch out). Finally, this integration led to a hydrocarbon discovery in a previously written-off fault block and proved a potential stratigraphic trap presence in this area. The well encountered 50 m of net gas-bearing sand within both intervals. This approach could further facilitate exploring stratigraphic play (s) in a similar geological setup.
Machine Learning for Facies Distribution of Large Carbonate Reservoir Models- A Case Study
Authors: Robail Frederic , Sanyal Satyashis , B M Noor Azudin Ahmad Nazmi , Koh Kwi Yen , Bt Hairon Nizar Farahani , Mohamad Rosli Ummi Farah 
Published: February 2023

AbstractMultibillion barrels oil in-place carbonate reservoirs have their unique static and dynamic modelling challenges due to the nature of the reservoir with both vertical and lateral heterogeneities. The complex geological processes which took place both during and after the deposition, results in the heterogeneity, which are reflected in the reservoir characterization of these large-scale carbonate reservoirs. Capturing the geological facies variability in the reservoir description is thus one of the critical elements to ensure the model's validity, robustness, and forecasting ability.This case study exemplifies the use of a machine learning approach to tackle this subsurface complexity within a multidisciplinary integrated study to construct a field scale reservoir model for a large carbonate reservoir.The carbonate field has recently acquired additional core data in several newly drilled wells. These cores have been described by sedimentologists to define reservoir depositional facies and lithofacies. This geological description has been used by a machine learning algorithm to train the conventional triple combo logs to recognize the reservoir facies. The training of the facies definitions at the cored wells were also conditioned to the sequence stratigraphic correlation framework of the reservoir. Later these geological facies have been propagated using logs to more than 80 un-cored wells to provide facies predictions within a geological context.The result from the machine learning algorithm gives an excellent replication rate on the cored wells. It is also robust on the un-cored wells throughout the field. This robustness of the facies definitions has been verified using production / injection log survey (PLT / ILT), core CT-Scan and core descriptions. Firstly, for the cored wells, the production / injection zones identified by PLT surveys clearly correspond to the best reservoir facies. Secondly, in the un-cored wells, the best facies predicted by the machine learning algorithm correspond to the production / injection zones interpreted from the production and injection logging surveys (PLT survey)The predicted geological facies in both cored and un-cored wells and seismic inversion trends were used to condition the 3D distribution of the facies in the reservoir model. The use of machine learning for facies prediction has also helped to validate the underlying geological concept of an older good quality reservoir interval in certain areas of the field, which were not adequately sampled from the existing core data.In the future, the machine learning based reservoir models will be used to identify new infill locations where "best producing" facies are likely to be present.
Numerical Investigations on the Doublet Huff and Puff Technology to Extract Heat from the Geothermal Reservoirs and Storing of CO2
Authors: Gudala Manojkumar , Tariq Zeeshan , Yan Bicheng , Sun Shuyu 
Published: February 2023

AbstractIn this work, we studied the implementation of huff and puff technology to extract heat from the geothermal reservoir. Two-dimensional numerical investigations were carried out using a fully coupled two-phase thermo-hydro-mechanical model with dynamic rock and fluid properties. COMSOL Multiphysics (a finite element solver) was utilized to build the model. The CO2 geofluid is injected in a supercritical state in a water-saturated geothermal reservoir. The results were showing promising for the extraction of heat and storing of CO2. In the simulation model, we designed a well pair (two-vertical wells) system with two different operating perforations in the same well with huff and puff cycle operation, and this technology is named as Doublet Huff and Puff (DHP). Injection wells operating at the top of the formation and production wells are operating at the bottom. The injection well-1 and production well-1 are operating at same time (i.e., 2 years). During this period, injection well-2 and production well-2 are ideal, and injection well-1 and production well-1 are ideal while operating injection well-2 and production well-2. This process is continued till the whole reservoir is saturated with the injected CO2 and/or the reservoir temperature reaches 60 % (i.e., geothermal reservoir life) of its original temperature. The CO2 plume expanding throughout the reservoir effectively while extracting heat from the reservoir. The sensitivity of well distance, injection temperature, injection velocity, and perforation length on the production temperature was investigated. The production temperature stays stable and high for a long time and no influence on the production temperature. Thus, the proposed technique (DHP) can be implemented for sequestering large amounts of CO2 along with heat extraction in geothermal reservoirs.
Successful Delineation of Sand Bodies to Improve Carbon Capture and Storage Reservoir Modelling, A Case Study from the North Malay Basin, Gulf of Thailand
Authors: Limpornpipat Orapan , Uttareun Ratchadaporn , Satitpittakul Ananya , Kunpitaktakun Takonporn , Henglai Puntira , Wongpaet Khuananong 
Published: February 2023

AbstractThe objective of this study is to characterize sand reservoirs by using seismic inversion technique, the results were used to support CO2 storage potential identification and reservoir modeling works (storage volume calculation). The key storage targets are the saline aquifers and depleted reservoirs. These main targets were interpreted as a deposition of distributary channels occurring in the Paleo Chao Praya delta plain during Miocene. The results of this project contribute to a more accurate volume calculation for CO2 storage capacity. A rock physics feasibility analysis was carried out to understand a link between the observed seismic responses and the rock properties. Based on conclusions made in the rock physics analysis, P-Impedance could be used to delineate sand reservoir from shale, thus, a post-stack deterministic seismic inversion was selected for this reservoir characterization. Bayesian litho-classification method justifies lithology types by Probability Density Function (PDF) of P-Impedance, the resulting PDF was then applied to the inverted relative P-Impedance to create sand probability and lithology (most probable) volumes. Then, posterior validation of the lithology classification results was performed by investigating the match between the actual upscaled lithology log and pseudo lithology log from the Bayesian classification. Furthermore, the sand probability maps of the target reservoirs show an acceptable sand distribution response to the distributary channels in lower coastal plain environment that is consistent with the well results. The results of this work demonstrate how quantitative interpretation (QI) can successfully improve confidence in sand reservoirs mapping, in an area of complex faulted reservoir interval. The results presented here are beneficial for storage potential identification and reservoir modeling part, which can provide a more precise estimation of CO2 storage volume. The final results of the QI study provide good quality seismic inversion products and lithology cube, which enabled sand delineation at the target CO2 storage level. The key contributors have been ensuring optimal seismic input data, being in this case achieved through using a PSDM seismic processing technology, careful parameterization of seismic inversion process, and utilization of Bayesian classification method for lithology classification.
Integration of Deep Resistivity High Definition and Ultra-Deep Resistivity 3D Inversion Enables Geo-Steering in Thin Laminated Reservoirs
Authors: Elkhamry Ayman , Fouda Mohamed , Taher Ahmed , Bikchandaev Eduard 
Published: February 2023

AbstractIntegrating the inversions of simultaneously acquired deep and ultra-deep logging while drilling (LWD) azimuthal resistivity measurements can improve the resolution of the overlapping volume under investigation and reduce uncertainty in the far field volume model reconstruction. Both are key tools for precise placement of horizontal wells, the recent enhancements in the downhole tools include surface processing algorithms and advanced visualization techniques that allow higher confidence in well placement decisions through improved understanding of subsurface geology and orientation of sand channels in real-time.The high-definition multi-layer inversion capability of a new generation deep resistivity tool has been utilized along with the 1D and 3D ultra-deep resistivity inversion for a separate established tool, providing detailed visualization of formations both near wellbore and in the far field. Both technologies were compared in reservoirs with varying resistivity profiles and thicknesses. In addition, the resistivity anisotropy analysis from ultra-deep 3D inversion was utilized to confirm lithology around the wellbore differentiating anisotropic shale zones from other lithologies of similar low resistivity. Ultra-deep 3D inversions were processed with fine scale cell sizes and then used to validate the high-resolution deep resistivity inversion results.The integration of multiple inversions with varying capabilities enabled resolving thin reservoir layers in a low-resistivity, low-contrast environment, providing superior resolution within the overlapping volumes of investigation of the deep and ultra-deep resistivities. Customization of the ultra-deep 3D inversion successfully enabled geo-mapping of 1-2 ft thick layers and was used to validate the high-resolution deep resistivity 1D inversion. The increasingly challenging geo-steering decision-making process in a complex drilling environment was addressed by employing the advancement in LWD technologies providing higher signal to noise ratios, multiple frequencies and transmitter-receiver spacings augmented with customized inversions providing superior results.This paper demonstrates the added value, to identify, map and navigate thin reservoir zones. A novel workflow has been developed to improve resolution in deep and ultra-deep resistivity mapping, enabling the identification of thin laminations around the wellbore capitalizing on the latest advancements in LWD geo-steering technologies.
A New Approach for Multi-Fractured Horizontal Wells Productivity Prediction in Shale Oil Reservoirs
Authors: Huang Dianyuan , Lian Zhanghua , Yan Guang , Ye Kai , Tao Liang , Zhang Jun , Halifu Mirinuer , Cao Zongxiong , Deng Xianan , Zhao Yuhang 
Published: February 2023

AbstractThe continental shale oil reservoirs usually have strong heterogeneity, which make the law of fracture propagation extremely complex, and the quantitative characterization of fracture network swept volume brings great challenges. In this paper, firstly, the grey correlation analysis method is used to calculate the correlation coefficient between different parameters and microseismic monitoring volume (SRV), and the key factors affecting SRV are identified. Secondly, the relationship between key geological engineering parameters and SRV is established by using the method of multiple linear regression, and the relationship is further corrected by productivity numerical simulation method, and the empirical formula for quantitative characterization of fracture network swept volume(FSV) is established. Finally, according to the field production of big data, the fitting chart of the accumulated oil production and the FSV is established, and the production of horizontal well is further predicted according to the fitting formula. The study results shown that the main factors affecting the SRV were fracturing fluid volume, fracture density, brittleness index, pump rate, horizontal stress difference, net pay thickness and proppant amount.The FSV in the study area was positively correlated with the cumulative oil production of the horizontal well. With the increase of the FSV, the accumulated oil production increased at first and then tended to be stable, and the optimal FSV was 760 ~ 850*104m3. The prediction method was verified by the typical platform in the field to be accurate and reliable. It can provide scientific basis for the productivity prediction of horizontal wells in shale oil reservoirs.
Innovative and Tailored Refracturing to Rejuvenate the Largest Shale Gas Field of China: Case Study
Authors: Li Yuanzhao , Ming Yue , Zhang Chi , Han Rong , Guo Luhao , Zeng Yunxiang 
Published: February 2023

AbstractThe Fuling shale gas field in China is one of the largest shale gas fields outside of North America. After a long period of production, some gas wells showed significant production decline and an effective refracturing treatment is designed to rejuvenate production. This Casing-in-Casing (CiC) refracturing method with customized design was successfully implemented with production increase beyond expectation. It was the first successful trial of a CiC refracturing treatment on a horizontal shale gas well in China.Bullheading (BH) refracturing with diverting balls was attempted in this field in past years, with high initial production observed. However, the production was inconsistent and declined quickly. The operator investigated and decided to attempt a CiC refracturing method in an under-stimulated candidate well. The CiC refracturing method is to cement a 3.5-in. liner in the legacy 5.5-in. casing to isolate the perforations, and new plugging and perforating (P-n-P) operations can be performed in the reconstructed wellbore. A refracturing design was customized integrating the production profile, residual recoverable reserves, and the specific 5.5- × 3.5-in. reconstructed wellbore limitation.During the BH refracturing, the treating pressure gradually increased with the drops of diverting balls, but the proppant placement became more and more difficult accordingly. The BH refracturing was finally completed with five cycles which was less than the design. High initial production after the BH treatment was observed but it declined quickly. In the CiC refracturing, a new 5.5-in × 3.5-in wellbore was constructed successfully, and it passed pressure test for stage frac. New clusters were added between the original clusters and stimulated with more intensive fracturing treatment in the new wellbore. Dissolvable particulate diverting agent was used for even cluster initiation and fracture geometry. The pressure response of the diverting agent was obvious indicating good diversion result and new reservoir contact. The initial production recovery rate was 88.1% compared to that of the original fracturing and the production increased 819% compared to the production before the CiC refracturing, which was much higher than BH refracturing.This case study illustrates that the CiC refracturing method is an effective method for refracturing. It overcomes the uncertainty and difficulty of proppant placement, achieves a higher intensity of fracturing treatment, and enables stimulation of the previously bypassed reservoir to improve the recovery of the well. The method has provided the industry with a new and reliable option to rejuvenate the aging wells.
A Hybrid Physics and Active Learning Model For CFD-Based Pipeline CO2 and O2 Corrosion Prediction
Authors: Yang Huihui , Lu Ligang , Tsai Kuochen , Sidahmed Mohamed 
Published: February 2023

AbstractPipeline corrosion induced from CO2 or O2 is a serious and costly hazard for oil/gas industry. CO2 and O2 are different complex corrosion processes. We developed an innovative hybrid model that combines both the first principal physics and advanced machine learning (ML) method to build a single model that can predict multiple corrosion mechanisms involving CO2 andO2. It can significantly speed up corrosion analyses in complex geometry using computational fluid dynamics (CFD). The ML prediction output was used to account for the local effects of mass transfer limitations, which requires only four variables: average inlet_velocity, pipe_ID, pipe_bend_angle and the ratio of pipe_bend_radius and pipe_ID instead of the 7 variables including CO2 partial pressure, pH value and temperature. The last three variables were found to be almost independent on the local flow variables because CFD solutions were only obtained at the macroscale level while the microscale surface variable values are solved using mass transfer limitation correlations. This new approach greatly lowered the number of CFD simulations needed to generate data for machine learning models. The hybrid model is about 106 times faster than the CFD simulation with acceptable accuracies.