bluuebunny/crossref_metadata_2025_split huggingface dataset:
Successful Water Conformance Control Using Newly Developed Smart Polymer Gels for Water Shutoff Application
Authors: Sharma Hemant K. , Almohsin Ayman M. , M Subaie Fehead , Kharrat Wassim 
Published: February 2024

AbstractWater conformance is a common challenge in oilfield industry especially in water flood and active water drive reservoirs where water production has a significant impact on production economics, oil recovery and facilities constraints. Water shut-off (WSO) is an essential solution to delay or minimize water production however effectiveness depends on treatment efficiency. Selective mechanical water shut-off, (i.e. through tubing bridge plug with cement on top, mechanical plugs), the main technique used to solve this problem so far, has disadvantages, mainly sacrificing the section below the plugs in spite of having hydrocarbons below the plugs. A new and innovative treatment solution using chemical water shut-off was developed in-house, which when used, led to minimizing the unwanted water production with enhanced oil production consequently helping to improve economic life of wells.The novel and fit-for-purpose chemical water shut-off technology extends the limitations of the currently available materials. The new system is designed to chemically bond the polymer to the rock surface. So, the system will have enhanced stability with superior performance. The fluid system is low-toxic and environmentally friendly. It is comprised of polymer gel and adsorption components. The gelant can be placed as a single phase, low-viscosity solution into the targeted formation zones. The gelation process is activated by the formation temperature. In-situ gelation takes place and the gel plugs pore spaces, thereby limiting undesired water production. The system can effectively prevent water flow at wide temperature range. The gelation time can be controlled by tuning the fluid composition. It allows a predictable and controllable pumping time, ranging from hours to days at a given temperature.Post job, the well performance improved substantially with significant reduction in water production and consequent increase in the oil production. Production logs conducted pre and post operation have clearly confirmed shutting off the high permeable streaks which were contributing to high water production. With the changed production profile and rate test pre and post job, the technology was confirmed a success. In addition cleanout of the treatment fluid performed after the treatment, ensured full-bore accessibility of the lateral for future well intervention including logging operations.This paper will describe in details the novel technology and its application, solution selection process and the operational deployment of the chemical including real-time monitoring of the fluid, as well as precautions taken to increase the success percentage.
Integration of Rock Typing and Neural Network Techniques for Accurate Permeability Prediction in Heterogeneous Carbonate Reservoirs: A Case Study from Abu Dhabi Offshore Field
Authors: Elabsy Eslam , Soliman Ahmed , Deng Lichuan , Al Abed Sundos , Montani Hiroki , Al Suwaidi Maddiah 
Published: February 2024

Abstract
Permeability prediction or calculation in heterogeneous carbonate formations is a challenging task due to the complexity of the rock properties and pore systems that are difficult to characterize accurately. In this study an innovative and efficient approach developed and used to overcome this challenge by combining rock typing and machine learning neural network (MLNN) techniques to accurately predict the permeability of heterogeneous carbonate formations.
The supervised machine learning approach based on a neural network algorithm trained on a large dataset of offset wells data. The input data for the algorithm included various rock properties from core and well logs analysis data. The output of the algorithm was a prediction of rock types and their corresponding permeability values based on the porosity and permeability equations. The MLNN model was trained using a backpropagation algorithm and validated using an independent dataset to ensure the accuracy and reliability of the results. The trained model was then used to predict rock types and permeability values for new wells.
The results of the study showed that the approach of using rock typing and machine learning neural network outperforms other traditional methods in predicting permeability in heterogeneous carbonate formations. The predicted permeability values were validated against actual measurements from core data and formation testing mobility, and the results showed a good correlation between predicted and measured values and demonstrating the model reliability. The use of rock typing provides a more accurate characterization of the reservoir and helps to improve the prediction of permeability. The study also revealed that the rock types and permeability values varied significantly across the carbonate formation, and the neural network model was able to capture this heterogeneity accurately by learn the complex relationships between the rock types and petrophysical properties, which resulting in improved permeability predictions. The predicted permeability values were used to generate permeability maps that helped identify areas with higher permeability values, which can be targeted for well placement to improve hydrocarbon recovery.
This approach lies in the integration of rock typing and machine learning neural network to predict permeability in heterogeneous carbonate formations. This method provides an innovative solution to the challenges associated with traditional methods, which often fail due to the complex nature of carbonate reservoirs. The approach is applicable to a wide range of carbonate formations, and has the potential to significantly improve reservoir characterization and production optimization.
Development of New Family of Perforating Charges for Stressed Rock Following a New Industry Standard
Authors: Grove B. , MacGillivray J. , Lavery J. 
Published: February 2024

Executive Summary
We describe the development of a new perforating charge, and its ultimate qualification following a recently-released industry standard testing protocol. The updated RP19B Section 2 standard program (API, 2021) prescribes a total of 12 tests, wherein commercial batch perforators are shot into natural sandstone targets across a range of in-situ stress levels. Testing included rigorous preparation and documentation, and was third-party witnessed for full transparency.
Compared to a reference legacy charge (which had been originally qualified in a 19B Section 1 unstressed concrete target), the new charge improves penetration performance in the standard stressed rock target by up to 22%, while simultaneously increasing casing hole diameter by 32%.
The industry has increasingly recognized in recent years that charge performance in unstressed concrete does not reliably correlate to performance in stressed rock. This suggests that at least some concrete-optimized charges may not deliver optimal performance downhole. Therefore, the incorporation of a standard stressed rock target to charge development should lead to perforators better optimized for downhole environments, compared to charges optimized for unstressed concrete. Independent witnessing further increases confidence in the performance to be expected in actual wells. Improved charge performance downhole, in turn, should translate directly to more productive wells (or, in the case of injectors, wells with better injectivity).
The charge discussed in this paper is applicable to two common gun sizes – namely 4-5/8 (or 4-1/2) in, loaded at 5SPF, and 7-in (12SPF). As of this writing, the same methodology is currently being applied to the development of additional charges for other gun sizes, ranging from 2 to 7 inches.
Unsupervised Clustering on Paleochannel Detection with Curvature-Based Attribute
Authors: Hungund Bilal , Jiang Fan 
Published: February 2024

Abstract
This research focuses on the application of advanced seismic data processing techniques and unsupervised clustering algorithms to accurately identify and map paleochannels in the high-resolution Maui seismic dataset. Pre-processing steps were applied to enhance data quality, followed by the calculation of a positive curvature attribute used as input for multiple clustering algorithms. The study evaluates four state-of-the-art unsupervised clustering methods and employs Principal Component Analysis (PCA) for result refinement and differentiation. By analysing the effectiveness of the algorithms, we demonstrate that Fuzzy C-means and Birch provide optimal segmentation of paleochannels. This automated approach not only overcomes the challenges of noisy data but also opens avenues for similar applications in seismic interpretation. The research underscores the significance of the unsupervised method in facilitating efficient resource exploration, emphasizing the integration of PCA to enhance segmentation accuracy and reduce human intervention. This study contributes to the sustainable management and extraction of natural resources, underscoring the potential of advanced techniques in remote sensing analysis.
Use of Transfer Learning in Shale Production Forecasting
Authors: Misra Siddharth , Elkady Mohammad , Kumar Veena , Odi Uchenna , Silver Andrew 
Published: February 2024

Abstract
Production forecasting is vital in the oil and gas sector, empowering engineers with insights for effective reservoir management. This paper introduces the concept of Transfer Learning as a powerful tool in the domain of machine-learning-assisted production forecasting that accounts of 3D spatial distributions of three geological properties, namely porosity, permeability, and saturation, two completion parameters, namely hydraulic fracture height and length, and production constraint. Transfer learning efficiently leverages knowledge from one problem to improve generalization on another, especially when data is scarce and computational resources are limited. To demonstrate the utility of transfer learning, we evaluate two scenarios of transfer learning. The first transfer learning scenario demonstrates the generalization of the forecasting to cases with variable hydraulic fracture spacing using limited training data. The second transfer learning scenario demonstrates the generalization of the forecasting to cases with variable natural fracture spacing and natural fracture permeability using limited training data. Source dataset contained 2000 realizations, while the target dataset contained 20, 40, 60, 80, 100, 250, 500, or 1000 realizations to represent the scenarios of limited training-data availability. The study confirms the benefits of transfer learning when the training dataset size is small (generally less than 100 training realizations); however, under large training dataset size (around 500 or more training realizations), transfer learning is not needed. For the first scenario involving variable hydraulic fracture spacing, the use of transfer learning ensured that the source model can be trained on target dataset with 80 realizations for gas rate forecasting at an accuracy of 25% in terms of MAPE, and with 500 realizations for condensate rate forecasting at an accuracy of 12% in terms of MAPE. Similarly, for the second scenario involving variable natural fracture spacing and natural fracture permeability, the use of transfer learning ensured that the source model can be trained on target dataset with 250 realizations for gas rate forecasting at an accuracy of 24% in terms of MAPE, and with 500 realizations for condensate rate forecasting at an accuracy of 23% in terms of MAPE. This illustrates the potential of transfer learning in improving forecasting models with limited data using a well pre-trained model and enhanced hyperparameter tuning of the transfer learning model. For cases with 500 or more training realizations, transfer learning severely underperforms as compared to training a conventional machine-learning model from scratch. The paper explores two cases of transfer learning.
Innovative Solution to Dissolve Highly Challenging Iron Sulfide Scales
Authors: Charushin A. , Muhammad M. , Al Balushi I. , Albalushi S. , Hassan I. , Marine H.C. 
Published: February 2024

Abstract
Iron sulfide scales are an inorganic deposition, highly acid soluble, that block oil and gas wells, and are usually formed from H2S and reservoir/well reactive iron reactions, since HCl reaction with iron sulfide generates toxic and corrosive H2S gas, several alternative dissolvers including simple organic acids and more complex chelates have been utilized, our main objective is to evaluate the efficiency of a new synergetic of alternative dissolvers to provide economically satisfying treatments in Iron Sulfide scale defected wells.
To judge the dissolution capabilities of the new solutions, four different synergies have been evaluated for HPHT solubility considering the effect of pressure and temperature, screening soaking time and solution pH influences, with aid of catalytic additives at different scale to dissolver ratios to be able to conclude the optimum treatment design. Furthermore, corrosion testing for proposed treatment, and reaction product Fourier Transmission Infrared Spectroscopy (FTIR) were performed, to find out the treatment corrosivity and the functional group of reaction products.
An oil contaminated Iron Sulfide scale sample was tested, using different dissolver concentrations, with and without surfactant/solvent pre-soak, the best performed solution could achieve a minimum of 55% weight dissolution at 1:10 scale to dissolver ratio, a minimum of 61% weight dissolution at 1:20 scale to dissolver ratio, a minimum of 85% weight dissolution at 1:30 scale to dissolver ratio, by applying 6 hrs surfactant/solvent pre-soak and 24 hrs of static treatment soak, at 90 °C and 300 psig, the best performed solution was tested for corrosion and found corrosive at 250 deg F for 48 hrs on C95 corrosion coupon so that a compatible corrosion inhibitor was added to the treatment, the reaction products were reported water soluble. Based on the experimental data and recommended treatment design, fluid was pumped successfully and satisfying outcomes have been reported.
This paper presents novel and additive information to the existing body of literature on Iron Sulfide scale dissolution, the developed product is a blend of organic salts in cationic surfactant solution, that provides an outperforming alternate economic solution to currently used Iron Sulfide scale dissolvers with high affinity to other mixed scales, and at less concertation and soak.
Risk Mitigation and Drilling Time Reduction Practices with Optimized Wellbore Geometries Implemented in Offshore Shallow-Water Tertiary Wells in Gulf of Mexico
Authors: Barrera Edison , Perez Carlos , Gomez Jose Manuel , Alarcon Jose , Torres Maria Andrea , Zapata Abelardo , Laguado Pedro 
Published: February 2024

Abstract
Well construction operations in the Gulf of Mexico involve a high level of complexity in a high-risk environment. Although multiple operators and service companies have been drilling for decades and technology has evolved throughout this time, the wells targets become more challenging to drill and the technical level along with the economical benefit require new approaches to make projects profitable.
One of the key aspects to make drilling projects successful is risk evaluation and engineering applications to deliver producing wells efficiently. The offshore fields in the Gulf of Mexico require custom wellbore architecture and drilling practices to optimize time on well delivery without compromising well integrity standards and safety.
This paper describes the successful implementation of a performance enhancement process and risk mitigation strategies applied in optimized wellbore geometries in shallow-water fields in the Gulf of Mexico, for which customized de-risking engineering and drilling practices were introduced to accelerate well delivery. The subject fields in the Gulf of Mexico are considered as strategic in the development plan, in which the main drive is time optimization for early oil production, resulting in additional risk management planning.
Research on the Control Law of Balanced Oil Production of the Fluid Control Valve in Horizontal Section and a Case Analysis
Authors: He Zuqing , Zhao Jianguo , Zheng Haotian , Han Suo , Han Shuo , Huang Bensheng , Yuxi He 
Published: February 2024

Abstract
The existing balanced production model does not consider the characteristics of the fluid control valve and the influence of various factors on the opening, resulting in the lack of theoretical support or basis for the throttling control of the fluid control valve. Therefore, proposed a trapezoidal throttle valve with a large throttle differential pressure and anti-choking. On this basis, established calculation model of the flow rate and throttle pressure for horizontal well based on fluid control valves. Next, evaluated the throttling effect of the single-segment fluid control valve. Finally, analyzed the law of influence of the opening of fluid control valves at different positions in horizontal well on flow rate and throttle pressure of natural gas. It found that the maximum throttling differential pressure of the trapezoidal orifice reaches 0.395MPa, which is 18.98% larger than that of the traditional rectangular orifice. To achieve balanced gas production, the opening of the fluid control valve from the toe end to the heel end gradually decreases. The opening decreases with the increase of the differential pressure of each segment, and the maximum decrease is about 58.38%. The opening increases with the increase of valve distance, and the maximum increase is about 1.25%. Based on the flow rate and pressure model, a case is analyzed by dichotomy. After 5 times of adjustment, the difference between the production proportion of each production segment and the average production proportion is less than 2.7 %, and the balanced production is achieved. This case analysis provides an example basis for the previous theory and numerical simulation. The research results provide a theoretical basis for the fluid control opening control of horizontal wells and provide ideas for balanced gas production.
Casing Centralization—Planned Compared to Actual: A Kuwait Case Study
Authors: Fituri M. A. , Ali Y. , Al-Naqi A. , Saikia J. , Abdulrahim K. 
Published: February 2024

Abstract
Several reasons can contribute to poor cementation; however, proper casing centralization can lead to uniform mud displacement with cement and can mitigate some of the cementation problems. Cementing companies design their casing centralizations based on simulators, but they have never been able to validate quantitatively how their planned centralization compares to the actual, the only comparison made in the past was based on the overall cement quality measured by the cement evaluation tools. This can be reflective of the centralization program in some instances; however, in others, attributing quality to centralization can be misjudged or centralization may be mixed with other factors.
Not being able to directly quantify and evaluate each element in the casing and cementation program makes it challenging to improve or acknowledge each design. Relating the achieved cement quality to casing centralization gives inconsistent results. In many instances poor cement behind casing is because of other factors such as spacer design, fluid rheology, formation fluids, borehole geometry, etc., and is not only due to poor casing centralization.
Casing eccentricity or casing touching outer casing/formation is always observed on cement evaluation imaging logs, as they typically appear in a form of galaxy pattern. However, these galaxy patterns can only be considered as a qualitative indication of the casing being close to a casing or to a formation, as it will be shown later in the paper that galaxy pattern start to show on cement evaluation images when casing centralization is below 60%.
The new generation of ultrasonic flexural measurement tools can evaluate cement and at the same time provide third interface echo (TIE) measurement over 360°. The TIE measurement can evaluate casing centralization by evaluating the time between the first casing reflection (mud-casing interface) and the third interface reflection (cement formation interface) (Δt). When all the Δt values across all azimuths are equal, it is an indication of casing being 100% centered, and when Δt is 0 or small (casing touching formation) from any direction then casing is 0% centered. This Δt can also be converted to pseudo annulus thickness by assuming a velocity for the material behind the casing.
For an operator to improve their field casing cementation, a campaign was performed for evaluating casing centralization in multiple casing sizes for different well trajectories to set benchmark for future centralization improvements. Each casing had different centralizer size and different patterns.
Impact of Unsupervised Machine Learning and Seismic Attributes on Mapping Stratigraphic Traps
Authors: Alabbad Maitham , Alkhalifah Hussain , Awfi Sami 
Published: February 2024

Mapping lateral variations of reservoir and seal is a key step for stratigraphic trap exploration. Hard data such as well testing results, fluid samples, pressure data, core analysis, and wireline log signatures are often used as evidence of lateral fluid barriers and facies boundaries which provide the lateral seal element of stratigraphic traps. Mapping these boundaries is challenging and requires the use of seismic data, especially in clastic reservoirs where sand bodies with different pore pressures or hydrocarbon columns can be mapped and correlated laterally, although they may not be interconnected.
One of the common workflows is to rely on seismic amplitude variations as a proxy for reservoir quality. Another method uses absolute acoustic impedance (AI) volumes with specific cutoffs derived from the rock physics template for the lateral seal definition. Also, red-green-blue (RGB) color blends of decomposed frequencies can be used to show the geomorphology of the reservoir unit and indicate lateral variations in depositional environments. These techniques require a prior knowledge of facies character on seismic to define suitable cutoff values. A good well dataset covering all expected facies is needed for calibration. Unsupervised machine learning techniques, on the other hand, are useful tools that guide seismic interpretation of large 3D datasets and reveal subtle trends automatically. The different unsupervised machine learning algorithms and their application on seismic data have been discussed in the literature. Mingjun and Cheng (2018), Lubo-Robles et al. (2019), and Owusu and Raef (2022) describe its use in facies classifications.
In this paper, two different seismic waveform classification algorithms are used to map potential stratigraphic lateral seal based on conventional land 3D seismic data. The first algorithm is Hierarchical Classification. The second one is based on K-mean Clustering. Methodologies, strengths, and weaknesses of these waveform classifications are discussed in this study. The derived seismic facies are compared with other products from conventional seismic methods such as seismic amplitude extraction maps, AI geobody extractions, and frequency spectral decomposition RGB blending.