bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning Application on Seismic Diffraction Detection and Preservation for High Resolution Imaging
Authors: Bashir Yasir , Mohd Muztaza Nordiana , Abbas Babasafari Amir , Khan Muhammad , Mahgoub Mohamed , Moussavi Alashloo Seyed Yaser , Abdul Latiff Abdul Halim 
Published: February 2022

Abstract
Seismic Imaging for the small-scale feature in complex subsurface geology such as Carbonate is not easy to capture because of propagated waves affected by heterogeneous properties of objects in the subsurface. The initial step for machine learning (ML) is to provide enough data which can make our learning algorithm updated and mature. If one has not provided the multiple shapes of diffraction data, then your prediction of ML will be not accurate or even ML not able to detect the pattern of diffraction in the data. After the learning, our machine, the detection of the target is the crucial part that compares with the target and searches the specific signature in the given data. In this paper, we feed it with data in the form of the image and feature. Which can pass through the learning algorithm to predict the target. The idea of ML is to get the difference between your prediction and the target as closely as much possible. Which leads to the better preservation of diffractions amplitude in laterally varying velocity conditions. ML destruction is used for diffraction data separation as the conventional filtering techniques mix the diffraction amplitudes when there are a single or series of diffractions.
New Method to Simulate Digital Petrophysical Properties in Heterogeneous Carbonates Using Multiscale Micro Computed Tomography Imaging and Customized Laboratory Experiments
Authors: Drexler Santiago , Bastos Alves Rafael , Silos Vitor , Ferreira De Siqueira Milena , Toelke Jonas 
Published: February 2022

Abstract
Offshore Pre-salt carbonates are the most relevant assets in Brazil. Since they have only been in production for the last two decades, their petrophysical characterization is of utmost importance to build reservoir models that can effectively estimate reserves and forecast production. The rock and fluid complexity of these carbonates increase the timeline and uncertainty of special core analysis programs. In this context, innovative technologies, such as digital rock analysis, can support and strengthen the petrophysical characterization of this challenging carbonates. In this work, we propose a methodology based on petrophysical trends to improve the definition of representative digital volumes of a reworked carbonate sample from the Pre-salt. In addition, digital static and dynamic properties were simulated at the pore scale and then upscaled for the whole plug. The porosity, permeability, formation factor and capillary pressure results showed a positive match with laboratory mercury injection capillary pressure and routine core analysis. The upscaling procedure was key to capture the dominant features at both the micro (pore) and macro (plug) scales commonly found in complex carbonate rocks. This workflow optimized the use of rock and fluid samples, reduced the timeline of the special core analysis program to 60% and supported minimizing the uncertainty of the traditional laboratory programs by improving quality control.
Combined Use of Nearfield Hydrophones and Multiple Migration for Shallow Imaging
Authors: Damianus Fritz , Poole Gordon , Khakimov Oleg , Cambois Guillaume , Mahgoub Mohamed , Tansini Alessandro 
Published: February 2022

Abstract
Modern acquisition approaches are moving towards ocean bottom node (OBN) designs. By acquiring hydrophone and geophone data, wavefield separation of these data provides access to up-going and down-going wavefields, and powerful techniques such as up/down deconvolution can be employed to attenuate surface related multiples and the source wavelet. In shallow water regions, however, very shallow reflections are not recorded. This paper will illustrate how we can derive an image of the shallow subsurface jointly using near field hydrophone (NFH) data and multiple imaging.
Traditionally used for source firing QC, NFH data are gaining popularity for shallow imaging. Typically recorded with a sample interval of 1 ms or less, NFH recordings are higher frequency than OBN data, which are normally limited to 250 Hz. In very shallow water regions, NFHs mounted on the inactive source array may only record beyond-critical arrivals which have limited use for shallow imaging. In this paper, we will demonstrate the use of NFH recordings mounted on the firing array to overcome this problem. However, NFH data on the firing array often suffer difficulties when recovering low frequency reflections due to the extremely strong direct arrival. In addition, processes such as source deghosting and demultiple may be challenging due to the single-fold nature of the data.
Another method that is gaining popularity for shallow imaging is multiple migration. Compared to imaging with primaries, multiple imaging provides a larger illumination area with smaller incident angles. We propose to combine the low frequencies from multiple imaging with the high frequencies from NFH imaging to provide a high-resolution image of the shallow section.
To illustrate this, we use a shallow water OBN dataset acquired offshore Abu Dhabi in water depths of 3 m to 50 m, where complementary information provided by NFH imaging and multiple migration, and the combination of these images, has resulted in a broadband shallow subsurface section with improved spatial continuity and reduced sidelobes.
Artificial Intelligence and Machine Learning Used as an Enabler for Dynamic Risk Management
Authors: Hutchins Robin , Holbrough David , Jenkins Mark , Saini Prem 
Published: February 2022

Abstract
Applying big data, data science, business process automation (BPA) and domain expertise to operational and project risk in the upstream O&G space, will create a paradigm when applied to wellbore construction. A scalable, dynamic risk ecosystem enables seamless integration of risk into all aspects of the well construction process, a cornerstone of this approach is interoperability at a system level.
A lot of risk management is subjective, risk registers and mitigations are generated by workshopping with SME's. Risk scoring is often performed in a similar manner. The financial sector now applies data science techniques, for example in the fields of auditing and compliance. The aim of this paper is to discuss how these new techniques are integrated into a well-established existing risk management processes.
The starting point for digitizing the process is data. A ‘Risk Ecosystem’ was developed with a risk engine at its core linked to curated input data and providing outputs through dedicated GUI's and direct links into offline planning and real-time operational software. The risk management process has become dynamic and integrated through the value chain. A major change is the integration with the real-time decision making process.
Risk assessments combine analysis by hazard, risk, probability and barriers. When applying risk dynamically to well construction operations the barriers can be data, analysis, procedural and/or physical. The interplay of these barriers is the mitigation.
In the risk engine barriers can be managed by the probability of effectiveness and the uncertainty of the input and output data. Artificial intelligence (AI) and machine learning (ML) can now enable risk and mitigation identification, supporting probability and uncertainty to be analysed in real-time to provide dynamic solutions.
Data connectivity enables dynamic risk management where the risk updates when new data is available or an analysis is improved. The probability and uncertainty of analysis supports risk ranking. Dynamic risk management in planning leads through to risk management during operations, as risk changes and updates the source data can be revisited efficiently as it is connected through hazard, risk or mitigation. AI is triggered, by a change in the data, and the improved risk information is distributed through a revised mitigation scenario. The integration of ML recommends mitigations trained to global experience and continues to improve as SME's qualify mitigations.
Exploration of the interaction of data management, AI, ML and personnel for risk management is important, achieving the correct balance reduces risk, making risk management more efficient.
We propose that the improvement in data science for hazard, risk and mitigation identification supports the drive towards a risk management standard of a minimum risk analysis for the industry. In time this will lead to operational efficiency and consistency of performance.
An Efficient Approach for Automatic Well-Testing Interpretation Based on Surrogate Model and Deep Reinforcement Learning
Authors: Dong Peng , Liao Xinwei , Chen Zhiming , Zhao Hongyan 
Published: February 2022

Abstract
The artificial well-testing interpretation is a good tool for parameter evaluations, performance predictions, and strategy designs. However, non-unique solutions and computational inefficiencies are obstacles to practical interpretation, especially when artificial fractures are considered. Under this situation, a new deep reinforcement learning (DRL) based approach is proposed for automatic curve matching on vertically fractured well-testing interpretation.
Based on deep deterministic policy gradient (DDPG) algorithm, the proposed DRL approach is successfully applied to automatic matching of well test curves. In addition, to improve the training efficiency, a surrogate model of the vertically fractured well test model based on LSTM neural network was established. Through episodic training, the agent finally converged to an optimal curve matching policy on vertically fractured well-testing model through interaction with the surrogate model.
The results show that the average relative error of the curve parameter interpretation is less than 6%. Additionally, the results from the case studies show that the proposed DRL approach has a high calculation speed, and the average computing time was 0.44 seconds. The proposed DRL approach also has high accuracy in field cases, and the average relative error was 7.15%, which show the reliability of the proposed DRL method.
A Dynamic Workflow of Well Health Issue Prediction – Gas Leakage
Authors: Haq Bashirul , Al Shehri Dhafer , Hassan Zahid , Ahmed Iftekhar , Isah Abubakar , Afagwul Clement , Sofian Mohamed , Mohammed Nasiru S. 
Published: February 2022

Abstract
Due to the corrosive nature of sour natural gas, productions through tubing and casing are susceptible to corrosion, resulting in gas leaking. This issue causes massive production loss. Subsequently, well health monitoring and early detection of existing and developing gas leakage are essential for profitable gas production. Dynamic material balance technique estimates gas initial in place (GIIP) using wellhead or bottom hole pressures and gas rates data at flowing conditions during the production. This method is commonly utilized for estimating GIIP but does not apply to predict gas leakage issues. This work aims to add a leak factor term by modifying the dynamic material balance equation and build a mathematical platform to detect and validate the problem by applying the modified equation.
The new platform produces expected well behaviour using the revised equation and curve-fitting tool in MATLAB and then compares with the actual behaviour and detect gas leakage. The deviation from the expected and actual behaviour determines the issue. Each component of the model is validated using know values. After that, the total system is tested with known leakage data. Finally, the platform is applied in the active gas well, and the leak detection of the platform is reasonably well. The new workflow can notify the production engineers so they can take corrective measures about the issue.
Automatic Calibration of Complex 3D Discrete Fracture Networks using EDFM-AI
Authors: Liu Chuxi , Gupta Anuj , Ates Harun , Yu Wei , Li Ning , Sepehrnoori Kamy 
Published: February 2022

Abstract
In the realm of shale reservoir development and modelling, the problems of completion evaluations and long-term well performances are particularly important but highly challenging. Without suitable measurement metrics of completion designs and production potential, the operators have limited ability to quantify and optimize the quality of completion. Presence of natural fractures within shale reservoir adds higher level of complexity to an already challenging problem. Ability to integrate natural fractures is critical for reliable history matching and long-term production forecast. These challenges limit the accuracy of the traditional numerical methods and needs to be addressed. The objective of this study is to evaluate a novel numerical method called embedded discrete fracture model (EDFM) to non-intrusively model the contribution of hydraulic fractures and natural fractures. In this study, we tested and incorporated cutting edge artificial intelligence with our EDFM method (EDFM-AI) to perform automatic history matching. These improved numerical methods are utilized to handle the above challenges in a systematic manner. The new EDFM-AI workflow is applied to a naturally fractured shale reservoir. Key untertainty features for hydraulic fractures such as half-length, height, water saturation, conductivity, and cluster efficiency are defined, with various confidence intervals. Highly uncertain parameters for natural fractures, such as total number of fractures and their conductivity are also included. Through EDFM-AI workflow, it is found out that the total effective fracture flowing area is about 2.21 million cubic feet for this Eagle Ford horizontal well case study. Furthermore, the conductivity of natural fractures are 1/100 times that of hydraulic fractures, but these natural fractures can contribute 25% of the 30-year oil EUR. Finally, the visualization shows that the drainage radius increased from 200 ft at end of the history period to roughly 500 ft after 10 years of production, which differentiate the optimal well spacing at different producing time. By implementing this novel workflow, the vast uncertainties can be quantitatively analyzed. The successful application of EDFM-AI workflow demonstrates its benefits compared to other unconventional modelling workflows in the oil/gas industry.
Strategic Well Landing and Risk Mitigation in Heterogeneous Formation With Deterministic Automated Inversion and Remote Monitoring
Authors: Nguyen Viet , Vianna Armando , Steviano Karel , Zulharman Ahmad , Aristya Ramadhana , Lie Hendry 
Published: February 2022

Abstract
The K-W horizontal well was drilled to produce gas reserves from A sand and oil reserves from B sand. Accurate landing of the well in the target B sand is critical for the placement of the 6 1/8" lateral section within the reservoir. The main objective of reservoir navigation in the 8 ½" build section is to detect and map the reservoir top as early as possible to smoothly place the well 10ft TVD below the top of the B sand with an inclination of 85 degrees.
Several operational risks have been identified during pre-well feasibility study, including reservoir resistivity and thickness variation, presence of intra-shale, presence of faults, and depth uncertainty from old seismic data. To mitigate the risks, extra-deep azimuthal resistivity technology was included in the bottom hole assembly. The real-time reservoir navigation was carried out remotely with frequent updates on real-time log analysis and observations provided to the client via a 24/7 virtual meeting. The service provider implemented a newly introduced centralized reservoir navigation system, which provides quicker data handling and seamless job handover. Multiple deterministic inversion workflows with constrained parameters were set-up to process the real-time extra-deep resistivity measurement, maximizing reservoir detection and mapping capability.
As a result, the 8 ½" hole section of K-W well has been landed within the target formation. After the last marker, log signatures indicated good correlation with offset well data, extra-deep resistivity detected the presence of sand from a distance of approximately 34ft TVD. However, the automated inversion multi-layer mapping indicated possible reservoir pinch-out. A decision was made by the operator to further drop inclination and cut down through the entire formation. When the well crossed the expected reservoir top, extra-deep resistivity quickly decreased and log signatures indicated the presence of a thin sand before entering the basal shale formation, which was accurately mapped by inversions. In the post-well phase, deterministic uncertainty and the new statistical uncertainty analysis were run to evaluate the position of the main boundaries defined by extra-deep reading measurements. 1) deterministic approach is instantaneous and based on the linearized dependency between measurements and model parameters; 2) statistical quantification is based on the analysis of set of intermediate inversion models and includes industry-standard P50 visualization.
This paper presents a case history of implementing extra-deep resistivity technology to overcome geological uncertainties during well landing in a complex environment. The novel approach of utilizing multiple deterministic inversion workflows maximized detection capability and accurate mapping of the reservoir structure, allowing early risk mitigation even before entering the target reservoir.
Demonstrating the Performance of a Multi-Tubular Corrosion Inspection Tool in Alloyed Completions
Authors: Fouda Ahmed , Dai Junwen 
Published: February 2022

Abstract
With the increased demand for drilling deeper wells in harsh environments involving corrosive, briny waters and more corrosive crudes, completion engineers increasingly adopt more resilient materials for well casings than conventional carbon steel. These materials include alloyed steels, where ferrous steel is mixed with other non-ferrous materials, such as chromium and nickel, for increased strength and durability. Alloyed steel has a lower magnetic permeability than carbon steel and, therefore, generates weaker electromagnetic signatures when logged with electromagnetic pipe inspection tools. This paper demonstrates the performance of an array multi-frequency electromagnetic pipe inspection tool in scenarios involving alloyed completions using a simulated mockup test with known defects. The types of defects considered are circumferential with different combinations of overlapping and non-overlapping defects on well casings.
The pipe inspection tool uses the eddy current principle and includes two transmitters and eight receivers. It operates in continuous wave mode at multiple frequencies. Optimized transmitter-receiver spacing configurations and multi-frequency operation provide sufficiently diverse information to help assess metal loss in individual pipes for a wide range of configurations, including those with alloyed completions. The tool uses a sophisticated workflow of data-processing and inversion algorithms to decouple individual thickness information from the measured data.
A mockup test was designed to replicate typical alloyed completions used in deep water wells to assess tool performance in different scenarios. The mockup comprises an alloyed tubing and two outer casings, which are standard ferromagnetic steel pipes, with seven combinations of defects on the casings. The tool response is synthetically simulated using a finite element electromagnetic solver and the synthetic data are inverted for metal loss on each one of the pipes. The estimated metal loss for each defect was compared to the actual metal loss to assess the accuracy of the tool. It will be shown that in order to obtain high accuracy of metal loss estimation, the electromagnetic material properties of the pipes, including that of the alloyed tubing, must be estimated with sufficient accuracy. The information provided by this tool will enable regular inspection of deepwater wells for corrosion and other integrity issues with minimal downtime and intervention cost.
Optimization of Nitrogen Lifting Operation in Horizontal Wells of Carbonate Formation
Authors: Hashmi Usman Iftikhar , Osisanya Samuel , Rahman Motiur , Ghosh Bisweswar , Subaihi Maad Hasan , Kuwair Husam F. Bin , Fathy Ahmed , Afagwu Clement , Mahmoud Mohamed 
Published: February 2022

Abstract
Specialized operations such as Nitro-logging require nitrogen injection in the open hole. The operating parameters of a nitrogen lifting operation in the open hole depend upon several factors that need to be considered which are discussed and optimized in this paper. A decision matrix is created to show the optimum injection rate for nitrogen for different reservoir parameters. In addition, the effect of several completion components is also discussed that can aid in operation.
In this project, we examine these factors and deduce the optimum operational conditions for each case based on reservoir and well condition. The controllable operational conditions are gas injection rate and pressure. Different combinations of water cut (WC), reservoir pressure, gas-oil-ratio (GOR), and productivity index (J) are simulated in Wellflo® to predict production in each case. Optimum Economic Factor (OEF), calcultates the efficiency of operation in each case which entails a well's production rate, length of operation, and amount of nitrogen used. The optimum operational conditions are used to populate a decision matrix that incorporates all the examined factors.
The analysis of the decision matrix led to the identification of different trends between the operational conditions and reservoir parameters. The main findings were that a higher gas injection rate was required to lift higher WC wells, especially wells with WC greater than 60%. Moreover, wells with GOR less than 1700 scf/stb or wells with J lower than 1 stb/d/psi or wells with reservoir pressure below 2500 psi also required higher gas injection rates. The effects were direr when the well met more than one of these parameters. Additionally, as coiled tubing size increases from 1.25 to 1.5 to 2", liquid production decreased from 2250 to 1960 to 1280 bbl/d, respectively. Studying the back pressure from facilities showed that as pressure increased from 0 to 400 to 1000psi, liquid production reduced as well from 700 to 450 to 100 bbl/d, respectively.
Considerable economic reduction can be achieved if the operation is optimized as per the results of this project. Overall, the decision matrix eases the operation's planning and execution time while optimizing the quantity of nitrogen gas used.