bluuebunny/crossref_metadata_2025_split huggingface dataset:
Well Production Prediction Method Based on Multi-Factor Fusion Time Series Model
Authors: Zhang Yaqian , Li Xianjie , Cao Jinxin , Miao Chuanhui , Zhang Yuling , Zeng Shilin , Zhang Jian , Li Yiqiang , Liu Zheyu 
Published: February 2025

Abstract
Production forecasting is a crucial issue in oilfield development, holding significant importance for evaluating geological potential and adjusting development schedule. However, due to complex geological conditions, seepage laws, and manual operations, traditional methods often struggle to achieve good results. This paper proposes a production forecasting method based on Multi-Factor LSTM combined with decline curve analysis. The production curve of an oil well is first fitted using a decline curve analysis, and then the difference between the actual production curve and the fitted production is taken as the input production data for the LSTM model. Considering operations such as daily production time, pump frequency changes, and choke size changes, this paper introduces these factors along with well production as input data within a time window to achieve higher production forecasting accuracy. The results indicate that the Multi-Factor LSTM combined with the decline curve analysis can effectively predict future well production. By introducing manual operations, the model can capture correlations between manual operations and oil production, demonstrating higher sensitivity to production fluctuations and achieving a model accuracy of 0.97. This represents an improvement of approximately 9% in R² compared to the Single-Factor LSTM model. Additionally, the introduction of the decline curve analysis complements the deficiency of machine learning methods in predicting long-term, monotonic decline trends. Compared to a standalone LSTM model, the proposed model better captures the long-term production decline due to natural energy depletion and reduced remaining oil reserves in the production sequence. The proposed oil well production forecasting method based on the time series model with multi-factor fusion exhibits good adaptability for oil well production forecasting, providing a new approach for rapid and accurate forecasting of field production and subsequent adjustment of production measures.
Enhancing Seismic 2D and 3D Data Conditioning by Leveraging Machine Learning
Authors: Kumar Sayani , Shekhar Sushant , Jeff Osabuohien Osahon , Salomia Gabriela , Hasan Hesham , Assaf Georges , Tran Dong , Maria Mihai Monica , Velikanov Ivan , Biniwale Shripad , Mustapha Hussein 
Published: February 2025

Abstract
Seismic interpretation is an essential factor in the success of field development projects because it allows geologists and engineers to better understand subsurface formations and identify potential resources; however, noisy seismic data, especially in regions with complex geological structures, frequently hinder this process. Such noise emerging from equipment limitations, environmental interference, and inherent geological complexities can obscure essential geological features, making accurate subsurface imaging challenging and reducing the effectiveness of traditional interpretation methods.
This paper presents a machine learning (ML) method to enhance the conditioning of 2D and 3D post-stack seismic data by targeting noise and migration artifacts. These issues are often responsible for compromising the quality of seismic data sets. The method this paper presents leverages convolutional neural networks (CNNs) to identify and correct spatial inconsistencies within the data. With minimal preprocessing, requiring only the conversion of data into a 3D NumPy array format, this approach simplifies the workflow while improving data clarity and accuracy. Early statistical analysis of outliers and data normalization provides the ML model with stable inputs, allowing it to iteratively train on raw data and infer the original signal patterns in an unsupervised manner without the need for labeled data sets.
The model outputs a conditioned version of the seismic data, significantly reducing noise while preserving critical geological features. The results demonstrate that ML seismic conditioning improves fault imaging, horizon continuity in areas with low signal-to-noise ratios (SNR), and localized smoothing without compromising resolution. Furthermore, the method presented in this paper retains signal fidelity and relative amplitude strengths, ensuring that geological interpretations remain accurate and reliable. By automating the traditional manual task of data conditioning, the proposed solution optimizes interpreter workflows, allowing them to focus on higher-level geological analysis and decision-making. This approach ultimately increases the accuracy of subsurface interpretations and resource exploration, improving the overall quality and efficiency of seismic interpretation projects.
In addition, for 2D seismic, traditional methods of denoising rely on expert judgement using tools such as f-x deconvolution. 2D CNNs offer an efficient alternative, especially when training data can be synthesized and leveraged in supervised learning. This method can help the CNN identify and remove noise while preserving pertinent subsurface structures. This paper presents a 2D CNN architecture containing a model trained with synthetic seismic data to improve SNR of field seismic data; hence, interpretation.
Combining Variation Diffusion Model and Multi-Source Experimental Data to Establish Digital Cores for Reservoir Exploration and Development
Authors: Zhu Dancheng , Wang Lian , Qu Jianhua , Zhang Liang , Deng Rui , Jing Feng 
Published: February 2025

Abstract
Digital core technology is emerging as a transformative force in the realm of reservoir engineering, offering unprecedented capabilities for conducting detailed and non-destructive analyses and experiments that were previously unimaginable. Traditional approaches to constructing digital cores, such as physical experiments and numerical reconstruction, have inherent limitations in terms of cost, time, and resource utilization. To overcome these obstacles, a groundbreaking intelligent digital core construction method called MSED-GDL which integrates multi-source experimental data with generative deep learning algorithms has been developed. This innovative method leverages the power of multi-source experimental data and generative deep learning algorithms to enable the rapid and efficient reconstruction of three-dimensional (3D) digital cores. By integrating sophisticated components such as the construction of a digital core autoencoder, the acquisition of multi-source experimental data through advanced fluid simulation techniques, and the application of variational diffusion models, MSED-GDL facilitates the swift and accurate reconstruction of 3D digital cores in a matter of seconds. To validate the efficacy of this method, an extensive dataset of 10 million digital core samples was generated and subjected to rigorous micro-scale fluid flow simulations, resulting in the acquisition of valuable multi-source experimental data, including porosity and permeability. The results demonstrated a high degree of consistency between the reconstructed digital cores and the original data in multiple key parameters, underscoring the statistical significance and robustness of the MSED-GDL method. This breakthrough in digital core technology holds profound implications for advancing our understanding of subsurface reservoir characteristics, optimizing exploration and development strategies, and enhancing resource utilization efficiency in the oil and gas industry.
Deepest 12-1/4" × 9-5/8" Section Drilled by Casing While Drilling (CWD) in Sanga-Sanga Block
Authors: Chairunnisa Z. E. , Wadjid Y. K. A , Kamil R. , Lubis T. A. , Wicaksono N. T. , Prasetyo D. E. , Saragih A. M. , Malik M. A. 
Published: February 2025

Abstract
In drilling the surface section of Sanga-Sanga Block, the main hazards are loss circulation and shallow gas where Casing While Drilling (CWD) is a secured solution. Services has been implemented for the past 5 years with over 97 runs with a recent success of reaching maximum depth ever drilled using CWD of 2256 ftMD from initially drilling 1657 ftMD wells in this field. The case study will describe the planning and execution of this well.
CWD is utilized in Sanga-Sanga Block to drill the surface section which was able to eliminate additional operation of drilling a pilot hole saving 85 hours drilling time and 285,699 USD. The cost savings of CWD compared to conventional drilling increases with deeper depths drilled. Therefore, the collaboration with the Operator and the CWD services provider during the planning and execution stage was the key to reach the maximum depth drillable in this field.
The latest well managed to drill deepest depth of up to 2256 ftMD for 9-5/8" × 12-1/4" hole size in 37.4 hours without any incident. This was achieved by optimizing the cutting structure using 3D bit drilling simulation software, as well as improving the hydraulics by constantly monitoring and adjusting the flowrate throughout the run done by collaboration by CWD field engineers, operator, and mud engineers on board. These adjustments managed to enhance efficiency and durability. The maximum depth achieved is 35% greater than the previous record.
This paper shows the continuous growth of CWD application which has proven to be a solution to overcoming losses, shallow gas hazards and many other drilling problems used in vertical depths up to beyond 1657 ftMD. The learnings from this case could initiate and be used as a reference for even deeper application of CWD technology in the Sanga Sanga Field or in other field applications.
Implementation of Artificial-Intelligence-Powered Tools in Data Processing for Well Time Estimation and Risk Analysis in Mexico and Ecuador Projects
Authors: Szlapa S. , Douy C. , Sesboue P. , Erives J. , Telles J. , Delgado A. A. Benavides , Paccha J. , Gori F. , Mani A. , Essmat N. , Azuara O. 
Published: February 2025

Abstract
The introduction of artificial intelligence (AI)-driven tools has revolutionized offset well analysis, particularly in estimating well time for conceptualization and planning phases. Previously labor-intensive manual processes for analyzing legacy data have been transformed into digital predictions of activities, operational sequences, key performance indicators (KPIs), and risk estimations through a web application. This system comprises AI engines that model specific aspects of well construction, working with a user interface that enables control over data and domain parameters. Engineers can develop well designs and, using existing designs as references, the application computes KPIs, forecasts operational sequences, assesses risks, and estimates well durations. By employing probability distributions for each input and iterating the process multiple times, the application generates histograms for each output upon simulation completion. Before AI tools, engineers faced the daunting task of manually analyzing extensive drilling reports, a process that could take weeks. The new web application streamlines this into a swift, efficient process, drastically reducing time and enhancing accuracy. Tested successfully in projects across Mexico and Ecuador, the application has shown significant improvements in efficiency and competitiveness. These AI-driven tools not only save time but also reduce costs and carbon emissions, exemplifying their transformative impact on the oil and gas industry.
Earth Model Building Strategies for Hybrid Survey of 3D Towed-Streamer and OBN – Case Study from Gulf of Thailand
Authors: Chen S. , Hassan A. , Ming W. L. , Busrijal R. , Abadi W. , Sriiamsaard W. , Suheri S. , Hartono A. , Arbi S. , Andria L. 
Published: February 2025

Summary Extended Abstract
This paper discusses the processing of first 3D towed streamer and ocean-bottom node (OBN) survey acquired in shallow water areas within the Offshore Thailand. The objective is to obtain accurate velocities and seismic images at reservoir level while also capturing karst features around the basement. The very complex geology and poor resolution of the legacy image, create difficulties in mapping the low angle thrust fault and the karst feature in Permian carbonate section.
Influence of the Tan-Lu Strike-Slip Fault Zone on Depositional Facies Distribution within the Fault Terrace Zone of the Yellow River Estuary Depression, Southern Bohai Sea, Eastern China
Authors: Guan Dayong , Qian Geng , Yue Junpei , Pan Wenjing , Lu Weiyan 
Published: February 2025

Abstract
The Bohai area is China's largest offshore crude oil production base, with proven reserves of Paleogene clastic rocks accounting for less than 30%. This positions it as a crucial production field for the future. Currently, accurately predicting the spatial distribution of these Paleogene clastic rock reservoirs remains a significant challenge. Located in the southern Bohai Sea, the fault terrace zone of the Yellow River estuary depression is bounded by the Tan-Lu strike-slip fault zone, which traverses eastern China. Recent drilling has revealed considerable variations in the spatial distribution of Eocene sandstone within this zone. Accurately predicting the predominant depositional facies is crucial for assessing the potential of this area.
The research methodology includes comprehensive studies on sedimentation influenced by structural dynamics, underpinned by source-to-sink system analysis. Specifically: (1) Analysis of paleontological stratigraphy and stratigraphic contacts subdivides the Eocene strata in this zone into six depositional cycles, characterized by clear lateral superposition relationships and episodic sedimentary features. (2) Structural evolution studies from the Paleocene to Eocene reveal that strata characteristics were shaped by the compressive actions of the Tan-Lu Fault Zone during the early Eocene to Paleocene, and by extensional influences during the mid to late Eocene. (3) Paleontological evidence indicates that under structural compression, the early Eocene strata predominantly developed on the western side of the fault terrace zone, while the central and eastern parts exhibited less development.
(4) During the mid to late Eocene, under tectonic extension, fracture channels functioning as sand transport pathways were extensively developed in the central to eastern parts. The primary direction of sand supply, influenced by extensional fractures in the source rock area, was oriented towards the eastern part of the fault terrace zone, rather than the central and western parts.
The study demonstrates that the differential tectonic activities of the Tan-Lu Fault Zone, characterized by initial compression followed by subsequent extension from the Paleogene to the Eocene, impact not only the stratigraphic distribution but also dictate the dominant depositional facies in the study area. Based on this insight, a targeted strategy for zonal and stratigraphic well placement within the fault terrace zone has been implemented, yielding promising exploration results.
Reduced-Order Modeling for Fractured Reservoir Simulation by Use of Trajectory Piecewise Linearization
Authors: Gao Ming , Sun Wenyue , Xu Jianchun 
Published: February 2025

Abstract
Flow simulation for fractured reservoirs requires more computational cost than in conventional reservoirs, especially when fractures need to be explicitly characterized. Therefore, it is crucial to develop acceleration methods for subsurface flow simulation in fractured reservoirs. In this study, we use the embedded discrete fracture model (EDFM) for an accurate simulation of the flow behavior in fractured reservoirs and implement a reduced-order model (ROM) procedure as acceleration methods for this simulation. The procedure is based on proper orthogonal decomposition (POD) and trajectory piecewise linearization (TPWL). The POD method represents the states of the reservoir with a highly reduced set of bases, thus achieving the order reduction for linear solver in iteration. In order to achieve higher acceleration, the POD method is combined with TPWL, called the POD-TPWL method. POD-TPWL-based ROMs can directly obtain new solutions by linearizing solutions saved from previous simulation training without computing information such as Jacobin matrices. Therefore, this method can achieve more significant speed improvements. As variables in the fracture domain change more drastically than in the matrix domain, we improve settings such as the basis matrix in the POD-TPWL method so that we can accurately predict the variables in both domains and the production of the wells located in them. We tested the accuracy and robustness of the ROM with several test cases. The results show that the fractured system states maintain the required accuracy even when the schedules in the test case are significantly different from those in the training case. The speedup improvement using the POD-TPWL method is significant (depending on factors such as model size), and we achieved a 1155-fold speedup by applying the method to a 3D EDFM-based reservoir model.
Investigation on Dilation Mechanisms of Low-Permeability Sandstone Under Water Injection in Offshore Oil Field
Authors: Chen H. , Cao Y. , Yu J. , Ma Y. , Peng J. , Gao Y. , Li D. 
Published: February 2025

Abstract
Rock dilation technology by water injection is an alternative and novel method for the reservoir stimulation of the sandstones in the oil fields west of the South China Sea. In this paper, triaxial compression and permeability experiments, physical simulation experiments, and numerical simulation were used to uncover the mechanisms of the dilation and permeability enhancement for the low-permeability sandstones. It was found that the strain softening occurs, and the permeability increases to 4 times when the volumetric strain is -12% under the effective confining pressure of 0.2 MPa. The strain softening and shear dilation tend to be more obvious under lower effective confining stress. Making the sandstone saturated can decrease the cohesion and increase the internal friction and dilation angles. As for tensile parting dilation, many microfracture networks take place, and the permeability increases by 2 orders of magnitude at most. The physical simulation revealed that the stepped-shape pore pressure preprocessing, low temperature, and cyclic oscillation with 0.5 min have a positive impact on the dilation effects. The numerical simulation of the old well showed a good fitting effect of BHPs and exhibited an effective dilation radius of around 12.83 m. For the new well, the BHPs and formation pore pressure were predicted, and the tensile-shear composite micro-fractures with a radius of about 40 m occur in the near-wellbore zone. This paper can provide a theoretical basis and technical support for the field operation.
Reservoir Prospectivity in Sentolo Formation Based on the Porosity and its Diagenesis: Analog Study
Authors: Assyifas A. A. , Dirgantoro M. F. , Giwangkara A. M. , Akmaluddin A. 
Published: February 2025

Abstract
The presence of a petroleum system in the South Central Java Basin represents a promising new hydrocarbon exploration environment. The existence of limestone in the Sentolo Formation can serve as a prospect reservoir rock. This paper will discuss the analysis of reservoir prospectivity levels in the Sentolo Formation based on the porosity and diagenetic process of analog outcrops, which is the most cost-feasible approach for initial exploration.
This research method consists of data acquisition, analysis, and synthesis. The data acquisition phase consisted of literature studies and field investigation. Field investigation included surface geological mapping, stratigraphic measurement, and lithological sampling. The following step is data analysis in the laboratory. Thin sections from five Sentolo Formation outcrop rock samples were examined using a petrographic image analysis method to determine the microfacies, diagenetic processes, and porosity. The last step is data synthesis, which mainly involves the integration and interpretation to determine the reservoir prospect of the Sentolo Formation based on outcrop data.
Limestone in the Sentolo Formation in the study area consists of Allochthonous Bioclastic Rudstone (01), Allochthonous Bioclastic Floatstone (02), and Packstone with Abundant Foraminifera (03). The depositional environments of 01 and 02 were in the open marine environment, and 03 was deposited on the slope. Based on porosity calculation, sample 01 has the highest porosity (21,89%), followed by 02 (17,8%) and 03 (16,9%), which are classified as good porosity (Koesoemadinata, 1980). The dominant types of porosity found are interparticle and vuggy porosity. The presence of vuggy and other secondary porosities implies the diagenetic processes. The observed diagenetic events in this study include marine, burial, dolomitization, and meteoric processes. The meteoric process is the primary diagenetic event responsible for the secondary porosity evolution in the study area. This event occurs in the early diagenetic process (Eogenetic stage). Comparisons are also made with the petroleum production system in the South Sumatera Basin, which bears characteristics similar to those of the South Central Java Basin. The porosity of the Baturaja carbonate reservoir rock in the South Sumatera Basin ranges from 5.4% to 11.6%, whereas the porosity in the study area reaches 7% to 21.89%.
Detailed Geomechanical Characterisation and Application of Shallow Shale Gas Reservoirs with Complex Tectonic and Multiple Fracture Systems
Authors: Zhang Muyang , Xian Chenggang , Huang Xiaoqing , Liu Yunyi , Liu Yang , Wang Jianjun , Liu Cheng , He Yong 
Published: February 2025

Abstract
In the Haiba Anticline Area, characterized by its shallow depth and significant tectonic stresses, a diverse spectrum of fracture systems is present. This paper explores the block complexity through 3D geomechanical modeling and verification. The study quantitatively characterizes the planar stress state and its heterogeneity. Based on data from multiple wells, the formation overpressure mechanism is judged, and a 3D pore pressure model is established. The equivalent mechanical models of different levels of fracture systems are established by numerical simulation experiments. Finally, the 3D geomechanical simulation is carried out through the Visage simulator on the Petrel platform, and the quality control is combined with the field information. The plane complex stress state is quantitatively characterized by Q index, and a new Stress Non-Uniformity index is introduced to quantitatively characterize the plane stress inhomogeneity of the block. By integrating the geomechanical model and key geomechanical characterization parameters, the stability of natural fractures under different stress conditions was evaluated, and the multi-stage and multi-cluster fracturing scheme was optimized. These findings are crucial for efficiently exploring and developing marine shallow shale gas in southern China.