bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comprehensive Analysis for Production Prediction of Hydraulic Fractured Shale Reservoirs Using Proxy Model Based on Deep Neural Network
Authors: Han Dongkwon , Kwon Sunil , Kim Jeongwoo , Jin Wooseong , Son Hanam 
Published: October 2020

Abstract
The proposed method is a model using data-driven modeling. As many databases are available, we propose new alternatives to production analysis using exploratory data analysis (EDA) and deep neural network (DNN) techniques. It is an economical and time-saving model and workflow that can replace and alternate the traditional physics-based modeling. In this study, field data used about 1239 wells from Montney shale formation in Alberta, Canada. Through EDA, we verified the correlation between each variable of datasets and data distribution, and 1143 wells were used as training data for DNN model through data preprocessing such as outlier analysis, scatter plot, etc. The database used for the study was collected through database of GeoLOGIC systems ltd. The data here is largely divided into three features. In the case of well information, completion and fracturing data, production data and well information, there are true vertical depth, latitude, longitude, and well direction. total proppant placed volume. The production data used as the dependent variable in the DNN model is cumulative gas production for 12-month. Comprehensive machine learning techniques were applied to further improve predictive performance and prevent overfitting problem. First, we analyzed EDA and statistical analysis with various variables related to productivity. Second, developed model were designed and data preprocessing was performed to select input variables that are highly correlated with the output variables. Third, through variable importance analysis based on random forest (RF), gradient boosting tree (GBM), extreme gradient booting (XGBoost). We found a parameter that is highly correlated with cumulative gas production. Finally, we proposed the applicability of categorical variables. Then we performed hyperparameter optimization, a difficult problem for the DNN model. This paper is Not only is this paper intended to propose a robust model, but it also provides the insight that petroleum engineers can use as proxy models to replace complexity reservoir modeling or estimate approximate productivity of shale reservoirs before drilling.
DTS Flow Profiling for A Horizontal Multi-Stage Fractured Well in Tight Gas Reservoirs by Improved Model Considering Open-Hole Packer Completion Scenario
Authors: Sui Weibo , Zhang Di , Cheng Si , Zou Qilin , Fu Xiaosong , Ma Zehao 
Published: October 2020

Abstract
With the gradually increasing applications of fiber-optic distributed temperature sensors (DTS) in unconventional resources exploitation, academic researchers have developed general theoretical models for forward temperature simulation and inverse flow rate profiling during and after stimulation workover. However, there have been no enough field applications for the established theoretical models and some practical issues still exist such as different completion scenarios are still lack of consideration in current models. This paper presents a DTS flow profiling case for a horizontal multi-stage fractured well in tight gas reservoirs with open-hole packer completion scenarios by applying a newly improved theoretical model.
In this paper, for the modeling part, we start with the semi-analytical wellbore-fracture-reservoir coupled flow/thermal model but improve it to consider open-hole packer completion scenario. Compared with the conventional cased, cemented and perforated completion style, the fracture initiation points in open-hole stimulated well are more effected by near wellbore in-situ stress field. Therefore, the open-hole packer completion possibly forms a two-fold flow regime. The formation fluid firstly flows through the fracture into the open-hole annular space between formation and the packer liner, then flow along the annular space until meet the frac port on the production pipe. The two-fold flow regime results in double temperature drops due to Joule-Thompson cooling effect. The original theoretical model is improved by adding a simulation sub-region representing open-hole annular which helps to understand the flow and heat transfer inside it.
With the improved mathematical model, DTS monitoring data during a three-rate production test in a horizontal multi-stage fractured well in Erdos Basin of China was simulated and analyzed. The improved model with open-hole packer completion was applied and then the gas flow profiling was accomplished.
Full 3D Reservoir Mapping Using Deep Directional Resistivity Measurements
Authors: Thiel Michael , Omeragic Dzevat 
Published: October 2020

Abstract
Deep directional resistivity logging-while-drilling technology and its measurement processing are instrumental in strategic well placement, landing, geosteering, and reservoir understanding. However, since the measurements are truly at the reservoir scale, reservoir complexities limit the applicability of 1D and sometimes even 2D interpretation models and inversions. A new full-3D imaging inversion is introduced to accurately map reservoir sections where 1D and 2D models are not adequate to represent the reservoir. The imaging inversion uses a 3D EM simulator and accounts for both resistivity and resistivity anisotropy and is fully consistent with the 1D and 2D inversion results in sections of lower complexity. The inversion is validated with a realistic 3D reservoir model and applied to field datasets from the North Sea, revealing complex 3D detail consistent with all the data.
Intelligent Completions Design and Production Optimization: Application Reality Now Versus the Future In Digital Oilfield
Authors: Goh Kim Fah , Biniwale Shripad , Musayev Rashid , Ahmed Elfeel Mohamed 
Published: October 2020

Abstract
Intelligent Completions (IC) are deployed with the high hopes of frequent data utilization and zonal selectivity maneuver to optimize production continuously. The permanent downhole presence of measurements like pressure, temperature, rate, water-cut, gas-break provide downhole indicators and trending analysis of production performance and injection conformance. These are utilized not only to maximize hydrocarbon production but also to reduce surface handling of water and/or gas, improve injection efficiency, and reduce carbon and environmental footprint. However, the reality could be different from the evaluation stage to the application stage. The asset production engineers or the reservoir engineers face real challenges when it comes to design, downhole installation, data transmission, real-time analysis, and optimization to deliver the real value of the initial investment. These suboptimal application factors, multiplied by the complexity of IC deployment and execution with existing hardware constraints, have limited the progression towards digital well technology. By analyzing such trends, a new advanced completion optimization methodology has been devised, leveraging the latest technology and innovation, IC deployment simplification, and electrification efforts in the industry.
This paper analyses the underutilization reasons of digital well technology, such as - the ability of design and implementation, the downhole data measurement, complexity of modeling and optimization, and the bottlenecks in applying the learning from the Intelligent Completions data to optimize production. It is then compared to the easing transition to the future digital-wells, advanced modeling capabilities that are driving the oilfield digitalization by next-generation Intelligent Completion. This digital transition ranges from ease-of-deployment to ease-of-optimization and eventually towards cloud-enabled decision making. The new era of IC electrification deployment and digital solutions are twinning to provide an integrated platform to maximize value and justification for more future digital wells.
A fully digital system to control reservoir and optimize the product is becoming a reality with the transformation of modeling capability and enabled by simplification of IC deployment, and this is the digital future of IC optimization. This digital solution is continuously feeding asset subsurface, modeling, and optimization team with productivity or injectivity indexes and other inputs required for reservoir steady-state and transient evaluation. The IC industry continues to be integrating into the new solution frontiers of logging-while-producing, the testing-while-producing capability to the eventual optimizing, modeling-while-producing future, leading towards a true digital oilfield of the future.
Production Optimization: Understanding and Solving Scale Formation in Mature Gas Wells
Authors: Schenkel Mara Lis , Irazuzta Verónica Laura , Candia Gisella Anahi , Scianca Norah , Aguero Sergio David 
Published: October 2020

Abstract
Since 2015, scale problems have appeared when gas production decreased below 30 Mm3/day and wells could not evacuate liquid and solids to the surface due to liquid loading. Scale depositions were found in casing and tubing at different depths close to Quintuco Formation perforations. During 2019, more than 30 wells were mechanically cleaned using slick line to remove scale every 30 days approximately. This paper describes the study of the origin of scale in gas wells producing from multiple formations and the chemical technology applied to solve this problem in the mature urban field Centenario (Neuquén - Argentina).
Core and water analysis were performed to study the scale phenomenon at the productive formations and to determine the compatibility between fracturing and formation waters. In addition, it established the scale origin and served to detect further wells susceptible to suffer scaling problems. Scale is produced in the casing, not in the formation, due to changes of pressure and temperature related to the expansion of gas and changes in water mix compatibility at Quintuco perforations depth. Due to the field location, subsurface safety valves were installed in the tubing to prevent possible blowouts. Thus, capillary string for product injection was discarded. A proven chemical technology for oil wells-encapsulated scale inhibitors-was adapted to treat gas wells aiming for a treatment duration of three months. The method consisted of a mechanical clean-up followed by a batch treatment of encapsulated product with water and, finally, a slick line run to ensure it reached the bottom-hole. The chemical treatment pilot was carried out in 6 wells.
Candidates were selected based on abnormal production decline, scales showed after slickline intervention, favorable response after mechanical clean-up, and high intervention frequency. The pilot held in 2019 showed an increase in gas rate production of 12 Mm3/day on average representing 40% of well production, high stability in gas production and a reduction of slickline intervention frequency. The duration of the treatment was initially estimated according to the liquid rate of the well and the product concentration used. During 2020 the pilot will be expanded to 32 wells in the field.
Solid scale inhibitor for gas wells is relatively new in the industry and there is not much bibliography about application and results in this type of wells. This paper describes the methods used for scale characterization, the prediction of its formation in gas wells, the selection method for scale inhibitors, the product application technique to the wells and the results obtained in the pilot, demonstrating its technical and economic feasibility for field use.
Hybrid Well Pattern to Unlock Yan’an Tight Gas Reservoir, Ordos Basin, China
Authors: Wang Xiangzeng , Zhang Lei , Zhang Tao , Qiao Xiangyang , Wang Yongke , Zhao Xisen , Li Xiangfang 
Published: October 2020

Abstract
Yan’an gas field is a typical tight sandstone reservoir with multiple gas layers, large vertical span, variable size of effective sandbody, high heterogeneity, and complex surface ground environment. Due to the depositional background including both continental and marine face, the tight gas reservoir shows marginal characteristics with low porosity/permeability, thin sandbody thickness and low lateral continuity. If the field is recovered by the current mainstream technology "horizontal drilling+multi-stages hydraulic fracturing", the subsurface reserves will not be controlled fully, resulting in low economic benefit. With over a decade of practice, a cost-effective hybrid well pattern adapted to each layer and each region was formed which shows its efficiency in development of Yan’an gas field. In the hybrid well pattern, directional (vertical) well is adopted to fully control the reserves of the four main gas-bearing layers, while horizontal well is adopted to maximize the production for the reserve-enriched layers. Unlike the uniform well spacing for the marine tight gas reservoir, the well spacing in Yan’an gas field is non-uniform to adapt the strong heterogeneity in terms of storage and percolation, and the methodology to quantify the well spacing according to the local reservoir property was discussed in detail. To decrease the sand-lost drilling risk in such a low sand-continuity reservoir, the strategy named "sand recognition while drilling" is proposed for the well placement. The mathematical models for designing the directional (vertical) wells and horizontal wells are also presented in this work. In addition, the non-technical factors such as loess plateau topography and coal/water/oil resources overlay give large difficulties to open the well site and extended reach wells are required, and the criterion in selection different types of well group was given mathematically as well. The above factors in constraint the well pattern are summarized with property constraint, local constraint, macro constraint and environment constraint. The hybrid well pattern successfully unlocked and maximized the marginal gas field.
Long Term, Periodic Aerial Surveys Cost Effectively Mitigate Methane Emissions
Authors: Sridharan Sri , Lazarus Aaron , Reese Carrie , Wetherley Erin , Bushko Katrina , Berman Elena 
Published: October 2020

Abstract
Results of multiple years of periodic aerial methane surveys over Pioneer Natural Resources’ operations footprint, comprising approximately 680,000 acres in the Permian basin, are presented, including impacts to operational efficiency, cost, and methane emissions mitigation. Aerial methane detection was performed using a light-aircraft mounted, integrated methane imaging spectrometer. Geo-referenced methane emissions data combined with real-time geo-referenced optical imagery provided accurate methane localization and source attribution. Ground inspection teams used optical gas imaging technology to validate the aerial results and dispatch repair teams. Externally validated leak quantification provided by the spectrometer further allowed accurate measurement of methane mitigation. Aerial methane inspections of nearly 10,000 operations sites per survey, including wells, tank batteries, and all associated equipment, are reported for multiple years of periodic surveys. The data shows a complete picture of the most significant methane emissions from the Pioneer operations footprint over consecutive years and has proven beneficialinvaluable for enhancing operational efficiency. Based on the data, Pioneer has been able to identify the areas of highest impact and focus operational resources on those improvements. Surveys identified types of emission sources that can be addressed immediately within Pioneer operations and areas where Pioneer would need to work with others to improve overall gas takeaway challenges in the Permian basin. Furthermore, Pioneer has reduced leak detection and repair (LDAR) costs significantly by reducing both driving time and ground-based inspection time. We estimate more than 2500 work hours and 1000 driving hours, were saved by each aerial survey. Between 2016 and 2018, the company's methane intensity has declined approximately 41%. Aerial survey results have allowed Pioneer to significantly reduce methane emissions while simultaneously improving safety and efficiency, reducing costs, and reducing vehicle traffic. To our knowledge, this is the first multi-year, comprehensive, aerial periodic methane survey of an entire upstream oil and gas operation's footprint. We're now able to report on the benefits of this paradigm shift away from conventional LDAR surveys. Although the challenge of reducing methane emissions can be daunting, the results from aerial monitoring show that with a technology and data-driven approach, operators can significantly reduce emissions while simultaneously reducing costs and improving operational efficiency.
Treatment of Losses Based on Root Cause and Fracture Width Analysis
Authors: Zhang Jianguo , Sant Randall , Majidi Reza , Edwards Stephen , Rodgerson Alan , Last Nigel 
Published: October 2020

Abstract
Lost circulation (LC) is extremely costly to many drilling operations due to loss of mud and productive time. Significant industry resources have been devoted to providing recommendations for both preventing and curing losses. These comprise bridging particles, settable pills and a focus on the better understanding, measurement and control of downhole pressures. The industry has also started to examine the relationship between downhole losses and fracture geometry. However, there has been very limited verification and utilization of such effort.
Knowledge of the loss mechanism(s), location of the thief zone and fracture geometry is fundamental for understanding and treating losses effectively. The proposed method introduces a systematic approach for diagnosing lost circulation into potential root cause(s) by utilizing up to twenty-two diagnostic indicators. This allows the most appropriate treatments to be mapped to the suspected root cause. This technique supports the creation of preventative strategies during well planning. For example, if induced fractures are identified as the most likely root cause in the planning stage, then a wellbore strengthening formulation based on the fracture width estimation is recommended.
A proper treatment can be designed based on the fracture width analysis as the data becomes invaluable in determining optimum sealing and effective treatment for losses into conductive natural fractures/faults. In case of loss events, the fracture width can be estimated based upon loss rate, the mud rheology, bottom hole pressure and pore pressure.
Field experiences demonstrate that natural and induced fractures are the two most common root causes of losses. For induced fractures, fracture width usually develops beyond 3000-micron (µm) once losses have occurred and the preventive lost circulation materials (LCM) have not then worked effectively. If the bottom hole pressures can be kept below fracture propagation limits (or far-field minimum horizontal stress), then induced fractures will not cause significant volume of losses. The drilling induced fractures can be modelled by using in-situ stresses, well trajectory, bottom hole pressure, geothermal and mud temperature information.
Data-Driven Rate Optimization Under Geologic Uncertainty
Authors: Sen Deepthi , Chen Hongquan , Datta-Gupta Akhil , Kwon Joseph , Mishra Srikanta 
Published: October 2020

Abstract
We propose a novel approach for rate optimization during a waterflood under geologic uncertainty in reservoir properties such as permeability and porosity. The traditional approach typically involves several runs of the forward simulator. This may not scale well when the optimization is to be performed at the full field-level and over multiple geologic realizations. A machine-learning (ML) based approach which is quick and scalable for rate optimization over multiple geologic realizations is proposed instead. The training data for the model is generated by running the forward simulator with randomly assigned well rates using multiple geologic realizations. A reduced order representation of the permeability heterogeneity in each of the realizations is derived using a grid connectivity transformation (GCT). This step involves finding basis functions corresponding to the different modal frequencies of the grid connectivity represented by the grid Laplacian. The projection of the heterogeneous property field along these basis functions gives the basis coefficients that form the reduced order representation. Subsequently, for each training datapoint, streamlines are traced and the minimum time of flight (TOF) representing the tracer breakthrough time at each producer is recorded. The basis coefficients and well rates are fed to a machine learning model as input and the minimum TOF at the producers forms the output of the model. This trained model can then be used along with an optimizer for computing the optimal injection rates to maximize the injection sweep efficiency. This corresponds to minimizing the variance in the minimum TOF within each well group. Different architectures of neural network are tested using 5-fold cross validation to decide the best ML model to compute the streamline time of flight. The trained model is used to perform well rate optimization over multiple realizations of geology by using a risk tolerance penalty. The optimal well rates thus obtained are compared with two cases: a) equal well rates assigned to all injectors and producers and b) well rates obtained by optimizing over a single realization without considering the uncertainty in geology. The optimal well rates are seen to offer better oil recovery and sweep efficiency than both cases.
The workflow is tested for a 50x50 two-dimensional (2D) heterogenous permeability field and for the SPE benchmark Brugge field, and is seen to result in significant improvement in oil recovery and sweep efficiency. A single forward run of the trained ML model is faster than the conventional simulator by about 3 orders of magnitude, making the approach suitable for large scale field application accounting for geologic uncertainty. The parsimonious representation of geologic heterogeneity and the use of ML for forward modeling makes the approach highly scalable and well-suited for full field applications.
Interfacial Viscoelasticity in Crude Oil-Water Systems to Understand Incremental Oil Recovery
Authors: Saad Ahmed M. , Aime Stefano , Mahavadi Sharath C. , Song Yi-Qiao , Patzek Tadeusz W. , Weitz David 
Published: October 2020

Abstract
Improved oil recovery from asphaltenic oil reservoirs may provide the world with a significant source of lower-cost energy over many decades. However, the mechanisms through which the surface-active components in crude oil, such as asphaltenes and organic acids, affect incremental oil production are still unclear.
In this study, we investigate crude oil/water interfacial films using shear and dilational rheology for mechanical properties and Fourier-Transform Infrared Spectroscopy (FTIR) to better understand its molecular species present at the interface that contribute to the development of viscoelastic behaviors. Dilational rheology has proven to be more sensitive to early time development of elasticity. In contrast, shear rheology provided more insights regarding the formation of elastic films at the macroscopic scale and late time interfacial changes. The presence of salts such as sodium chloride in the aqueous phase played a critical role in altering the dynamics of both the rheological properties development and the interfacial tension.