bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fracturing Simulation and Design Optimization of Fracture Network in Non-Uniform Stress Field of Deep Shale Gas
Authors: Lin Chen , Zhao Jinzhou , Ren Lan , Lin Ran , Wu Jianfa , Wu Jianjun , Song Yi , Xie Mengke , Hu Zheyu 
Published: February 2024

Abstract
The deep shale is influenced by multiple tectonic movements, resulting in well-developed reservoir faults and a non-uniform distribution of the stress field near the wellbore. The volume of the fracture network may be impacted by non-planar, asymmetric, and irregular behavior during hydraulic fracturing. One important aspect influencing gas well productivity is the volume of the fracture network. In the complex structure of deep shale gas reservoirs, a model is established in this study to define the non-uniform stress field near faults and to predict the propagation of fracture network. By contrasting the models with microseismic monitoring data from the mining field, the models' dependability is confirmed. The impact of fault type, fault offset, length, height, strike, dip, and distance on the fracture network volume is quantitatively investigated, and the influence of fault characteristic parameters on the stress field is quantitatively calculated. The ideal stimulated reservoir volume (SRV) is the design objective for hydraulic fracturing inter-cluster spacing, which is optimized based on the fault characteristics of the Y101 well area in southern Sichuan. The findings indicate that the stress field rotates clockwise near reverse faults and right-lateral strike-slip faults, and counterclockwise near normal faults and left-lateral strike-slip faults. A bigger fracture network volume results from the small stress difference near normal faults and right-lateral strike-slip faults, whereas a smaller fracture network volume results from the significant stress difference near reverse faults and left-lateral strike-slip faults. The following is a ranking of the fault structural parameters that have an impact on the volume of the fracture network: dip &gt; strike &gt; displacement &gt; distance &gt; height &gt; length. In the case of I fault, the ideal distance between clusters is smaller the closer to the fault the larger the horizontal stress difference, which prevents the fracture network from propagating. When the strike angle for Grade II, III, and IV faults is less than 50°, the closer the fault is to the fault, the larger the horizontal stress differential. This does not promote the fracture network's growth, and it also results in a smaller ideal spacing between clusters. When the strike is more than 50°, the ideal distance between clusters of perforations is bigger and the horizontal stress difference is less the closer the strike is to the fault, which promotes the propagation of the fracture network. Horizontal wells have to be positioned appropriately away from faults; a distance of more than 2000–4000 meters is recommended for level I faults, more than 1500–2500 meters for level II faults, more than 700–1200 meters for level III faults, and a closer distance is permissible for level IV faults.
Driving Efficiency Forward: Harnessing Digitalisation for Rig Energy Management
Authors: Saghir F. M. , Daif M. Y. , Alarfaj M. A. 
Published: February 2024

Abstract
The oil and gas industry has long been at the forefront of innovation and technology in its quest to optimise operations, reduce costs, and minimise environmental impacts. One of the paramount challenges in this industry is enhancing rig energy efficiency. The traditional approach to rig energy management involves manual monitoring and control systems, which are often cumbersome and less effective. However, the advent of digitalisation offers a transformative solution to this challenge. By leveraging digital technologies, including the Internet of Things (IoT), Edge Analytics, and Machine Learning, the industry can effectively track and manage rig energy consumption, equipment performance, and emissions.
This paper provides a comprehensive review of the use of digitalisation for rig energy efficiency tracking, exploring various technologies, methodologies, their impacts, and the potential for future advancements. It also addresses challenges, future perspectives, and the potential for sustainable and efficient energy operations in the oil and gas industry.
Advancing Relative Permeability and Capillary Pressure Estimation in Porous Media through Physics-Informed Machine Learning and Reinforcement Learning Techniques
Authors: Kalule R. , Abderrahmane H. A. , Ahmed S. , Hassan A. M. , Alameri W. 
Published: February 2024

Abstract
Recent advances in machine learning have opened new possibilities for accurately solving and understanding complex physical phenomena by combining governing equations with data-driven models. Considering these advancements, this study aims to leverage the potential of a physics-informed machine learning, complemented by reinforcement learning, to estimate relative permeability and capillary pressure functions from unsteady-state core-flooding (waterflooding) data. The study covers the solution of an inverse problem using reinforcement learning, aiming to estimate LET model parameters governing the evolution of relative permeability to achieve the best fit with experimental data through a forward problem solution. In the forward problem, the estimated parameters are utilized to determine the water saturation and the trend of capillary pressure. The estimated curves portray the relationship between relative permeability values and saturation, demonstrating their asymptotic progression towards residual and maximum saturation points. Additionally, the estimated capillary pressure trend aligns with the existing literature, validating the accuracy of our approach. The study shows that the proposed approach offers a promising method for estimating petrophysical properties and provides valuable insights into fluid flow behaviour within a porous media.
Energy Efficiency Application for Reservoir Pressure Management by Optimum Water Injection Strategy and Sustainable Low Carbon Surface Water Disposal
Authors: Susatyo Y. , Aghbari H. , Asmi H. , Lawati M. , Abri M. , Saadi F. , Hinai G. , Wardi A. 
Published: February 2024

Rima cluster fields are mature brown fields located in the south of the Sultanate of Oman (figure 1). They are on production for more than 40 years since 1980s and all production process at on central main production station called Rima production station.
Over many years of productions, All Rima station fields have developed high water cuts, average of 96% Water Cut (Figure 2). The Rima production station processes the incoming gross fluids, separating oil and water, dehydrating the crude for export, and partially treat water for bulk removal of oil in water.
The excessive produced water from Rima production station is disposed into Deep Water Disposal (DWD) wells. This disposal process requires a pumping station to boost the water pressure from atmospheric pressure to over 150 barg to be disposed deep in the disposal wells.
Solution for Improving the identification of Low Relief Carbonate Reefs in Shallow Water Areas
Authors: Zhang Hongjun , Du Houyu , Wang Jun , Cheng Hu , Shen Yang 
Published: February 2024

Pre-stack depth migration using high precision velocity model is an effective tool to restore the real structures under the variation of lateral interval velocity. Due to lots of seismic data in shallow water area with low coverage and large minimum offset, it is difficult to use shallow seismic data to invert the correct velocity, which will cause large velocity error to the targets when calculating deep velocity. Meanwhile, the frequency bandwidth of carbonate reefs is narrow owing to the absorption of muddy seabed and formation. How to improve the imaging accuracy and resolution of the carbonate reefs by the limited seismic data becomes the key to the reservoir research and subsequent drilling.
Two of the key challenges for velocity model building in the shallow water area with the interest targets of low relief carbonate reefs are a heterogeneous overburden, such as small scale low velocity channels and the presence of high velocity carbonates, which result in strong velocity contrasts. Two model building techniques have been presented in this paper to address these challenges. FWI has been shown to be highly effective in resolving overburden heterogeneity and high-precision tomography allows us to preserve sharp contrasts in our velocity models, TTI QPSDM solved the imaging accuracy of low relief carbonate reefs and broaden the frequency band.
Full waveform inversion has certain requirements on the accuracy of initial velocity model. The greater the velocity model error, the more serious the FWI local minimum caused by cycle skipping. On the other hand, the interval velocity model converted from RMS velocity with Dix formula lacks velocity details in shallow layers due to the small acquisition coverages causing hard to get accurate velocity from seismic data, and shallow logging data are not usually recorded by reason of limited investment and less target interest. How to get relative accurate initial velocity model, high quality shot gathers with rich low frequency and signal to noise ration improvement is important points in this paper.
Dynamic Resolution TLFWI to Resolve the Structure Distortion Beyond the Penetration of Diving Waves in Tuna PSC Block
Authors: Wang Jun , Chen Yitao , Deng Peipei , Yu Ziqin , Crabtree Nick , Mulyono Dono , Juliansyah M. Nauvall 
Published: February 2024

AbstractFaults and carbonates are well developed throughout the Tuna PSC block, offshore Indonesia, forming good traps for hydrocarbon reservoirs. However, the deep target depth with strong shallow overburden effects makes it challenging to resolve the real structure with traditional methods, especially when the available datasets are acquired with narrow azimuth and limited offsets. This paper demonstrates the application of Dynamic resolution Time-lag Full waveform inversion (DR-TLFWI), which uses both diving waves and reflections for velocity update with optimized low- and high-wavenumber components, to address the challenges in the deep part of the image that lies beyond the reach of diving waves. DR-TLFWI with dynamic weighting applied to different gradient components enables more effective utilization of the full wavefield. This approach succeeds in resolving the complex velocity field and improves the imaging of deep targets on the study data with a limited offset range. With DR-TLFWI applied, the imaging uplift results in less structural undulations and better mapping of the upthrown fault-bound closure, potentially reducing uncertainties in the subsequent exploration.
AI Based Unstructured Document Geotagging Solution
Authors: Pal Anik , Mishra Nirbhay , Gandhi Mihir 
Published: February 2024

Abstract
In the oil and gas industry, extracting relevant information for new projects often involves scrolling through a large unorganized or semi-organized unstructured electronic repository of unstructured documents inside an enterprise. However, the digital availability of these unstructured documents and their proper characterization with respect to E&P domain objects remain a significant challenge. To address this issue, we developed an - "AI-based unstructured document geotagging solution" that utilizes text mining, optical character recognition (OCR), natural language processing (NLP), and named entity recognition (NER) to extract entity relations for spatial E&P objects and document category (for example well completion, log data print, daily drilling report etc.) from documents content autonomously. We employed popular language models spaCy, Roberta-base, GPT2 and GPT3.5 and compared the results. The extracted information was then linked to a structured database that contained geo-locations. The solution was developed using the company specific data sciences platform but is capable of being deployed for an operator on-premises or in the cloud. The end-to-end solution can be integrated with any GIS-based dashboard solution, allowing user to select objects from the map and the model can predict related documents to the objects along with document category. This solution will allow user to obtain an organized list of related documents out of electronic repository with unorganized collection of uncharacterized documents, which will allow the user to prioritize and deploy only content of interest to be ingested to the insight and analytics system often hosted in cloud environment.
Supervised Machine Learning for Liquid Rate Estimation in Oil Field with Limited Well Monitoring Data: A Case Study of the Handil Oil Field
Authors: Setiadi Rahman , Hidayat Masun , Mardin Mursalim 
Published: February 2024

Abstract
Handil is an oil field located in the Mahakam swamp area. Approximately 80% of its wells are operated with gas lift. Well surveillance primarily relies on pressure, temperature, and gas injection measurements, which are locally updated through daily operator visits. In some wells, an additional single-phase orifice meter is installed on the flow line to estimate gas rates, but it doesn’t provide any information on liquid rates. Mobile testing units, mounted on floating barges, conduct well tests on a campaign basis every 3 to 6 months. However, daily liquid production monitoring becomes extremely challenging due to the lack of metering references at the well level. Furthermore, fast sedimentation area restricts the mobile testing unit to approach the well cluster which further extend the well potential’s uncertainties.
Supervised machine learning techniques were employed to estimate the liquid rates of the wells based on available surface parameters. Two machine learning models were developed: liquid rate prediction based on wellhead parameter data (well head estimate) and gas rate correction on water-out gas well based on orifice parameter data (orifice estimate). The preprocessed datasets consisted of clean 930 well test data for the well head estimate and 167 data for the orifice estimate. They were consisted of actual well production test data (wellhead pressure, temperature, gas injection rate, real gas rate, liquid rate, and water-cut), additional flow line parameters for the orifice estimate (flow line pressure, temperature, orifice differential pressure, beta ratio), and the open reservoir depth. The datasets were used to train and test different regression methods, including linear regression, polynomial regression, support vector machines (SVM), and random forest. Random forest regression, providing accuracy up to 93%, was selected on both ML model. Furthermore, additional polynomial regression was applied for model reinforcement, avoiding over-fit and flat prediction on the blind data beyond the training set range.
The results show that the developed machine learnings increase the field daily liquid rate production allocation accuracy at 90% from previously only 80% compared to real produced liquid, better detect liquid production decline or incline following dynamic well parameter for quick necessary action, provide new insight to sand production avoidance on specific sand control type, and accurately provide gas correction rate on water-out gas well.
This data analytic approach for liquid rate estimation through machine learning contributes to the portfolio of techniques used in the oil and gas industry. It is highly applicable and replicable in oil fields with traditional and limited well monitoring data.
On-The-Fly History Matching of Simulation Models Using Generative Diffusive Learning (SimGDL)
Authors: Alnasser Mohammed H. , Maucec Marko 
Published: February 2024

Abstract
History matching is an inverse problem in which a geological model needs to be calibrated under uncertainty in order to produce a representative reservoir simulation model. While there are many methods for computer-assisted history matching, most of them require substantial computational power and manifest slow convergence for large-scale simulation models. To tackle these challenges, we propose a new technique for history matching reservoir simulation models using generative diffusive learning (SimGDL) [1].
Our work consisted of several steps based on conditional denoising diffusion probabilistic models. First, we generated several thousands of geologic realizations covering a wide range of spatial subsurface uncertainties. Secondly, we added various amounts of gaussian noise to these realizations and trained a U-Net to predict the added noise based on the corresponding production response. When history matching, a certain amount of noise is added to one of the geologic realizations and then the trained U-Net, guided by the targeted production response, is used to iteratively remove the noise until it converges to a newly generated, dynamically calibrated geologic realization.
To test the potential of this new approach, we created a synthetic simulation model. Several thousands of permeability realizations were prepared and used to train the diffusion U-Net. The simulation model combines a single water injector and an oil producer, where oil rate, water cut, and bottom-hole pressure were designated as production responses. Dynamically calibrated permeability realizations were generated by adding noise to some of the existing realizations and then passing them through our diffusion U-Net, conditioned by the selected target production data. The simulated dynamic responses using the generated geologic realizations showed great improvements in matching the target production data. In addition, the posterior realizations were able to retain main feature signatures from the prior distribution and the areas to be updated. Furthermore, the generated models incorporate the quantification of uncertainty, as captured with multiple geologic realizations that were used in training.
This paper presents a new method for data-driven dynamic calibration of reservoir simulation models utilizing Generative Diffusive Learning. It incorporates a robust uncertainty quantification using an ensemble of representative subsurface reservoir models. By adding further complexity to conditioning parameters, this innovative method could be further extended and scaled-up to history match simulation models with diverse structural layouts and production profiles.
Pore-Scale Mechanism of Surfactant-Polymer Flooding and the Improvements on Oil Displacement Efficiency
Authors: Chen Shaohua , Wang Jinxun , Huang Tianping , AlOtaibi Mohammed B 
Published: February 2024

AbstractMany laboratory tests and field applications have successfully demonstrated that surfactant-polymer (SP) flooding is of importance and high efficiency to displace the residual oil trapped in the pores and capillaries of reservoir rock after water flooding. In this study, a micromodel experimental setup for visualized oil displacement was applied at high-temperature and high-salinity (HTHS) condition to study the synergy between a hydrophobically associating polyacrylamide (HAPAM) and different types of surfactants as mixed chemical surfactant-polymer (SP) flooding. Based on the results and captured micromodel images, the mechanisms of SP flooding to displace crude oil were investigated, and a most efficient SP flooding strategy was proposed. The physiochemical properties of the SP solutions were investigated before the micromodel displacement test. The micromodel slice was initially saturated with crude oil, followed by injections of a high-salinity water, chemical solution and subsequent water slug to displace saturated oil. During oil displacement, observed phenomena were recorded by a digital camara equipped on the micromodel device to analyze the pore-scale mechanisms of SP flooding, the viscosity alteration due to SP synergy and emulsification resulted from surfactant were also considered in the displacement mechanism. Among three surfactants, the anionic surfactant (AS) exhibited the most improved performance in oil displacement when it combined with the HAPAM comparing the other two surfactants, which is a non-ionic surfactant (NS), and a cationic surfactant (CS), respectively. The HAPAM/AS SP formulation showed a highest viscosity owning to hydrophobic association and electrostatic adsorption between HAPAM and AS comparing NS- and CS-related formulations, resulting in an enhanced sweep efficiency to displace more crude oil. Furthermore, the HAPAM/AS formulation exhibited strong emulsification to facilitate the formation of low-viscosity oil-in-water (O/W) emulsions to further enhance the flowable capacity of the crude oil, the O/W emulsion droplets can flow easily to pass through narrow throats and small or micropores by deformation along the flow, and eventually escape from the micromodel, resulting a higher displacement efficiency. Consequently, the highest production of crude oil was observed using the HAPAM/AS solution as a displacing agent comparing the other SP formulations.
Challenging Fishing Operation to Retrieve 1.68-In Wireline Tool in 6-In Horizontal Open Hole: Case Study from the UAE
Authors: Perkash W. , Alharthi R. G. , Maheshwari Y. K. , Subaihi M. H. , Franco E. , Zailani M. F. , Khan R. S. , ElAttar A. , Mostafa S. , Gul N. , Binoy A. 
Published: February 2024

Abstract
In the United Arab Emirates, an operation to retrieve a production logging tool that was lost-in-hole (LIH) yielded numerous insights for future operations. The fishing operation presented several challenges. First, the outside diameter (OD) 1 11/16-in. bottomhole assembly (BHA) was inside an internal diameter (ID) 6-in. open hole. The minimum completion ID was 2.635 in. The operation involved over 3,000 ft of horizontal open hole section in which the last 600 ft were facing upward.
Use of the Coiled Tubing (CT) logging equipped with a casing collar locator (CCL) played a crucial role in identifying the precise depth in the horizontal section of the production logging tool, CCL data combined with the caliper readings from a previous run helped to design the fishing BHA for retrieving the LIH tool. The designed BHA was equipped with a cutlip bell guide and an orienting tool with the aim to facilitate the latching. These datasets and challenges were considered by the intervention team in setting the most appropriate fishing strategy.
The LIH toolstring retrieval involved six runs. Initially, a conventional fishing BHA was used, but it encountered tagging issues at the gas lift mandrel (GLM). To overcome this, two straight bars were added to the fishing BHA increasing its length and avoid tagging at the GLM. During the second run, surface weight readings indicated that the LIH toolstring was being pushed along the open hole. In the third run, a similar BHA was used while pumping friction reducers. There was no evidence of the fish as the CT reached the end of the well. In the subsequent run, a lead-impression block (LIB) was deployed to detect the top of the fish. However, the LIB came without any visible marks. In the fifth run, the CT reel was replaced with an electric-cable-equipped pipe. The objective was to detect the fish in the open hole using the CCL sensor. Once the tool was identified, the engineering team modeled the fish using caliper readings and deviation survey data, enabling the proper design of the fishing BHA. The BHA was equipped with a cutlip bell guide and an orienting tool, which facilitated the latching and subsequent recovery of the tool in the following run.
With the insights gained from this operation, future operations in similar scenarios can benefit from the use of fiber optic-equipped CT. The proper interpretation of fish positioning, aided by caliper and CCL data, will allow for improved decision making in selecting the appropriate fishing tools and techniques. This, in turn, can lead to more successful and efficient retrievals of lost tools, minimizing downtime and potential operational disruptions.