bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Region-Based History Matching Approach Using Data-Driven Modeling Techniques
Authors: Mital M. , Verde A. , Saputelli L. 
Published: September 2016

Abstract
History matching is an important inverse problem extensively used to estimate petrophysical properties of hydrocarbon reservoirs by matching a numerical simulation to the reservoir's history of multiphase flow production. Modern history matching strategies pose the characterization process as an optimization problem involving the estimation of rock properties such as porosity and permeability in all grid blocks (Ng) of a petroleum reservoir model. However, when large number of cells are considered these strategies becomes highly inefficient requiring large number of computational expensive reservoir simulations and even being inadequate, from an optimization point of view, when static data is not abundant or unreliable leading to ill-posed situations.
This work proposed a novel approach to reduce the complexity of the inverse problem in several ways by: i) focusing exclusively on characterizing rock properties within certain regions of interest (Nr where Nr&lt;&lt;Ng) in the reservoir model (instead of at every grid block) where either high level of uncertainty exists or exploration wells are expected to be drilled, ii) parameterizing spatial distribution of rock properties using kriging techniques based on data available only at particular locations in the reservoir (in special at the center of predefined regions of interest), iii) replacing the time consuming reservoir numerical simulator by a surrogate model constructed using only input and out data, iv) efficiently generating multiple realizations of property distribution (input data) employing well-known design of experiments techniques and forecasting well production response (output data) associated to those realizations using an open source and multi-purpose numerical reservoir simulator tool (MATLAB Reservoir Simulation Toolbox, MRST®) as well as Ecrin®, and v) constructing a computational cheap surrogate model employing data dimensional reduction techniques.
Using synthetic two-dimensional and multiphase numerical examples of increasing level of complexity involving several thousands of unknowns, the accuracy and computational performance of the proposed region-based history matching methodology was evaluated. The novel approach reduces the size of the optimization problem to a fraction of unknown parameters and the results showed it can be used effectively and efficiently for estimating petrophysical reservoir parameters with acceptable accuracy when static reservoir data is limited in the absence of previous characterization efforts. The novel approach seems also flexible enough to incorporate known geological properties of the reservoir by constraining the optimization problem and the results can also be directly used as a first solution approximation in more detailed characterization projects.
Large Scale Testing & Modelling for Cement Zonal Isolation of Water Injection Wells
Authors: Therond Emmanuel , Bois Axel-Pierre , Whaley Kevin , Murillo Rodrigo 
Published: September 2016

Abstract
Water injection into soft sand is a global industry challenge due to the complex problem of maintaining sustained water injection rates into the desired reservoir. Drilling, Cementing and Completion engineers are addressing each technical and operational aspect of water injectors, including cement isolation. Cement serves as a barrier from well construction to post abandonment. It contributes to ensuring no out of zone water injection occurs due to flow behind casing. If water does go out of zone, new drilling hazards due to water breaching and loss of reservoir management will occur.
At present, as far as we know, the industry does not have a systematic methodology for defining and verifying the required physical and mechanical properties of the cement to endure water injection service and retain its isolation capability during well life. Cement integrity simulators (CIS) provide different answers, mainly because they all assume a d ifferent initial stress state in the cement after hydration. As a consequence, a new CIS model that computes this stress state has been developed, along with a large scale testing setup to validate its predictions.
The new model incorporates key design parameters of effective CIS models. 1) The initial stress state after cement hydration is computed; 2) Varying loadings the cement sheath is submitted to are simulated; 3) The elasticity, plasticity, and failure of materials is taken into account; 4) The simulations are fast enough to facilitate sensitivity analysis; 5) The model outputs allow visualization of cement integrity across the entire length of the cement sheath; adjacent to both reservoirs and seals.
In parallel to the modelling work, a large-scale test apparatus was built to evaluate cement zonal isolation under water injection pressure and temperature conditions. Its objective was to generate pressure and temperature cycles inside sections of cemented casing assemblies to replicate the conditions of pressure and temperature variations in a water injection well.
The results of the test confirmed the accuracy of the new CIS model. It also showed that cooling due to water injection had a bigger impact on cement integrity than increasing pressure. Additionally, it showed that micro-annulus generation had more effect than tensile cracking in terms of cement barrier permeability increase.
Based on this work, cementing design workflow for zonal isolation of water injectors has been established. As injection pressure and temperature, rock properties, confinement pressure and completion type vary from well to well, zonal isolation for water injection wells must be designed on a case-by-case basis. New cementing workflows for water injection wells involve rigorous cement mechanical property testing and cement stress modelling.
Horizontal Steam Injection Flow Profiling Using Fiber Optics
Authors: Shirdel Mahdy , Buell R. S. , Wells Mike , Muharam Cece , Sims Jackie 
Published: September 2016

Abstract
Steam conformance control in horizontal injectors is important for efficient reservoir heat management in heavy oil fields. Suboptimal conformance and non-uniform heating of the reservoir can substantially impact the economics of the field development, oil production response and result in non-uniform steam breakthrough. In order to achieve the required control, it is essential to have an appropriate well completion architecture and robust surveillance.
Five fiber optic systems, each utilizing a unique steam conformance control completion configuration, have been installed in two horizontal steam injectors to help mature steam injection flow profiling and conformance control solutions. These fiber optic systems have utilized custom designed fiber optic bundles of multimode and single mode fibers, for distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) respectively. Fiber optic systems were also installed in a steam injection test flow loop. All the optical fibers successfully acquired data in the wells and flow loop, measuring temperature and acoustic energy.
A portfolio of algorithms and signal processing techniques were developed to interpret the DTS and DAS data for quantitative steam injection flow profiling. The heavily instrumented flow loop environment was utilized to characterize DTS and DAS response in a design of experiment matrix to improve the flow profiling algorithms. These algorithms are based on independent physical principles derived from multiphase flow, thermal hydraulic models, acoustic effects, large data array processing, and combinations of the foregoing methods for both transient and steady state steam flow. A high-confidence flow profile is computed based upon convergence of the algorithms. The flow profiling algorithm results were further validated utilizing a dozen short-offset, injector observation wells in the reservoir that confirmed steam movement near the injectors.
A Study into Prediction of External Casing Corrosion from Contact with Overlying Aquifer and Injection Waters
Authors: Farooqui Mohammad , Marmoush Mohammed , Aly Maher , Pacheco Jorge , Morrow Timothy 
Published: September 2016

Abstract
This paper presents the methodology and findings from a study to assess the external corrosion rates in an offshore field from contact of the casing string(s) with aquifers and/or water injection zones overlying the production reservoir. The study objectives were to gain a better understanding of indicators of contact between the water and the casing strings and then to assess the corrosion risks that this presented.
A key indicator of contact between formation water and the casing was elevated Gamma Ray recorded across the interval when compared to a base log. The elevated Gamma Ray recording is indicative of scale deposition on the casing string and scale modelling tools were utilised to assess the potential of inorganic salt deposition on the casing strings. In the absence of reliable logging tools that can provide quantitative measurement of metal loss due to external corrosion through multiple strings, this study utilised a proprietary corrosion modelling software to assess the possible minimum and maximum corrosion rate range based on the limited information on water composition and the content of dissolved acid gases (H2S and CO2) in the formation fluids.
The paper provides suggestions to identify potential locations of contact between corrosive aquifer waters and the wellbore from routine production logging data. It identifies critical subsurface data required for a realistic assessment of external corrosion damage to the casing strings in the absence of effective logging tools to measure actual metal loss trends.
Machine Learning Approach for Irregularity Detection in Dynamic Operating Conditions
Authors: Sidahmed Mohamed , Bailey Richard 
Published: September 2016

Abstract
Designing systems that can detect irregularities in process monitoring data and which can be implemented efficiently is a challenge. The ability to detect and diagnose such events will help ensure stability of operations, avoiding excursions that cross thresholds reflecting safe operating limits and consequently improving process reliability and availability. Typically, using classical outlier techniques to detect anomalies assumes that a ‘normal' pattern of behavior, in a statistical sense, can be assumed and/or confirmed and used to identify what is an extreme observation. In practice, simple univariate measures of normality seldom result in effective solutions in systems whose dynamic behavior generates wide-ranging time varying data.
This paper presents a multi-criteria, data-driven approach for detecting irregularities in multivariate, time-series, dynamic systems with irregular changing patterns. The capability to automatically detect and flag such data will reduce the risk of crossing operational boundaries and improve levels of operations availability.
We developed a semi-supervised machine learning framework that provides a blue print for configuring application-specific irregularity detection. The framework takes into account the extreme dynamics typically encountered in Upstream operations that limit standard learning models' capabilities to accurately represent and hence draw inferences about the state of the underlying system. A hybrid, adaptive, non-linear, probabilistic, reasoning model learns iteratively over multiple operating windows to identify breakouts in typical behavior and to detect ‘remarkable' divergence of reconstructed signal from original space.
The method can identify single or multiple instances of irregular activity, since the underlying causal factors, resulting in such behavior, are likely to generate a number of irregularities reflecting nonconformity to normal pattern. Unexpected sequential changes in observed system behavior can be leveraged as a first-level filter of the basic anomaly signature. Results show that applying the framework to detecting irregularity in wells operations enabled near real-time flagging of irregular patterns. An operator's adjustable alert can be set based on a threshold value of the calculated score of the streaming signal.
This new approach is a significant departure from conventional methods of establishing and using baselines for normal process behavior, such as signature-based, profiling, fingerprinting, etc., to detect anomalies. Its key advantage is that it avoids the use of single-valued thresholds intrinsic to the competing methods of the types listed above. Such single-valued thresholds have the effect of limiting the practical application of such techniques in Oil and Gas applications.
Evaluation of Completion Practices in the STACK Using Completion Diagnostics and Production Analysis
Authors: Senters C. W. , Van Sickle S. , Snyder D. 
Published: September 2016

Abstract
Evaluation of completion techniques continues to help in cost reduction and optimization practices. The objective of this project is to evaluate completions within Oklahoma in the STACK (Sooner Trend, Anadarko Basin, and Canadian and Kingfisher counties), comparing several types of technology and optimization methods implemented within this play.
Changes to completion practices are driven by cost reduction and production needs. When evaluating a newly implemented technique, a "science well" is often selected to run a multitude of diagnostics to verify the effectiveness of the change. The dataset in this study goes beyond the concept of a single well, or several well analysis, and focuses on approximately 50 wells in this play. Wells are categorized based on completion practices and data was gathered based on completion diagnostics. A macroscopic production analysis was completed to complement the diagnostic results.
As with any study of this magnitude, there are several variables to capture and take into consideration while evaluating the diagnostic and production data. Wells in this study are categorized to maximize the number of unchanged variables. Observations were made on the overall stimulation coverage of each treatment interval evaluated, isolation between stages completed, and trends in production. Several examples of diagnostic data are presented as case histories in addition to the categorized data set.
The STACK is one of the four most prolific plays currently being drilled and completed in the United States. This is the largest case history published on wells completed in the STACK that utilizes an integrated daily production and diagnostic approach. The dataset and case histories in this paper provide valuable insight for operators and service companies who are completing wells in this play.
Perdido - A Five Year Look at the Technologies that have Enabled Ultra-Deepwater Success
Authors: Riley Chris 
Published: September 2016

Abstract
Discovered in 2002, Perdido is a Gulf of Mexico frontier ultra-deepwater development, and its success has been heavily dependent on several technologies: 4-D seismic for waterflood management, PMTs (pressure monitoring transducers) for subsidence monitoring, MPD (managed pressure drilling) to mitigate narrow drilling margin, and caisson ESPs for subsea boosting. Now, five years after first oil, these technologies have matured, and each contributes to a project that exceeds initial expectations.
Collaborative Working Across the E&P Lifecycle and Evolving With New Technologies
Authors: van den Berg Frans G , Awobadejo Mayowa , van Dijk Jaap , Weiss Chris 
Published: September 2016

Abstract
Collaborative Work Environments (CWEs) were implemented in the majority of Shell's assets. Collaborative working has evolved beyond production and surveillance processes and has been enhanced with new mobility and visualisation technologies.
Improved RBF-Based NMR Pore-Throat Size, Pore Typing, and Permeability Models for Middle East Carbonates
Authors: Shao Wei , Chen Songhua , Cheng Yuesheng , Eid Mahmoud , Hursan Gabor 
Published: September 2016

Abstract
A refined radial-basis-function (RBF) method with a forward selection algorithm to improve the stability of the prediction of pore throat sizes was recently reported by the authors. Subsequently, from the pore throat size distribution data, permeability and pore typing models were developed. These models were developed with the core samples from the high-to-medium quality reservoir sections of several Middle East carbonate wells.
Because the RBF is an interpolation method, the validity of RBF based petrophysical models is enveloped by the petrophysical parameter range that the core samples represent. For economic reasons, it is common practice that core analysis be conducted on high reservoir quality rock samples because they are most important to production. To apply RBF-based models for interpreting well logging data, it is important that such models be developed with a broad range of rock qualities to help prevent misinterpreting the lower-quality formation rocks.
To expand the application envelop of the RBF based nuclear magnetic resonance (NMR) permeability models, a new set of core measurements from different reservoir quality sections, as well as non-reservoir quality sections of several carbonate wells, are added to retrain the RBF-based NMR permeability models. Standard statistical validation methods are used to demonstrate the necessity and improvements of newly retrained RBF-based models. The new models are applied to well logging data with varying reservoir quality sections, proving that the new models are adequate for better permeability prediction of all rock quality formations.
Insights on Overflushing Strategies from a Novel Modeling Approach to Displacement of Yield-Stress Fluids in a Fracture
Authors: Osiptsov Andrei , Zilonova Ekaterina , Boronin Sergei , Desroches Jean , Lebedeva Natalia , Willberg Dean 
Published: September 2016

Abstract
Overflushing to displace the fracturing treatment away from the wellbore and into the fracture is common in multistage completions of horizontal wells in unconventional formations, but it can damage overall fracture performance by overly displacing proppant to leave the fracture unsupported or allowing gravity slumping to occur, again leaving unpropped area. A novel modeling approach was investigated to gain insight into the process and determine reliable bounds for overflushing.
From a fluid mechanics viewpoint, overflushing is displacement of a Hershel-Bulkley fluid by a power law fluid in a Hele-Shaw cell, leading to Saffman-Taylor instability at the fluids' interface. Most hydraulic fracturing simulators use power-law rheology. A novel numerical model accounting for the yield-stress behavior exhibited by proppant-laden slurries was developed using the lubrication approximation and includes transport equations for fluid volume fractions and a nonlinear elliptic equation for pressure with mixed-type boundary conditions. The pressure equation is solved with a fixed-point (Picard) iterations method, with the multigrid linear solver used at each iteration.
The model was validated against three sets of experiments in Hele-Shaw cells: gravitational slumping, displacement of Newtonian fluids with fingering, and displacement of a yield-stress fluid by a shear-thinning fluid, leading to formation of fractal fingering patterns. Good agreement is obtained. Based on fracture mechanics, we analyzed what portion of the fracture may be left unsupported before it is severely pinched during drawdown as a reliable bound for overflushing volumes. A parametric study was focused on the displacement of the yield-stress slurry by the overflushing fluid, varying overflush volume and rate and rheology contrast between slurry and displacing fluid. Qualitatively, when fingers of the overflushing fluid can be created at the overflush/slurry interface, large slurry pillars are preserved in the near-wellbore area, which may keep the fracture open; gravitational slumping is mitigated by the yield stress. Three scenarios occur, depending on slurry rheology: from slumping-dominated (low-viscosity base fluid in the slurry), through intermediate to a fingering-dominated scenario, where gravitational convection is suppressed by high viscosity of the slurry (~1 Pa s) and considerable yield stress (&gt;1 Pa). The third scenario minimizes the geomechanical risks of overflushing by providing tiny fingers that are unlikely to be pinched out during fracture closure.
From the study, bounds for reliable overflushing are proposed, which are a lot more restrictive when the slurry base fluid does not exhibit a yield stress (e.g., for slickwater treating fluids).
The Radial Approach to Safety Factors for Tubular Design
Authors: Liu Zhengchun , Klever Frans , Samuel Robello , Gonzales Adolfo , Kang Yongfeng 
Published: September 2016

Abstract
Traditionally, collapse safety factor (SF) is the ratio of tubular collapse resistance to collapse load. The collapse resistance is usually calculated using formulas suggested in API 5C3 or ISO 10400. If the collapse load is very small while the tension stress and/or internal pressure are high, the collapse SF could be very large, which is unrealistic because the load point is close to the collapse envelope. This paper presents a radial approach for the collapse SF calculation to overcome this problem. Moreover, the application of this radial approach can be properly extended to the calculation of the burst SF and the connection SF for arbitrary load combinations.
The new SF is named the "radial safety factor." It must meet the following requirement: when all the tubular loads (such as axial force, internal pressure, and external pressure) are scaled by this SF, the load point must fall exactly on the failure envelope. Therefore, for collapse, the new SF must simultaneously satisfy (1) API 5C3 collapse formulas, and (2) the through-load-point radial line equation. The Ridder's method is used to solve the SF for a specific load case. The algorithm has been implemented in a computer program and integrated with commercial software. This radial approach has been applied to both pipe body and connectors.
For burst, it is shown that the radial approach to calculate the SF is equivalent to the current method of comparing the von Mises equivalent stress to the material yield strength. An example case is used to study the effect of the radial approach on the collapse SF values. The collapse SF value is observed to become much smaller when the effective axial stress (actual axial stress plus internal pressure) is very high, which is expected because of the much-smaller radial collapse resistance (at the cross point of radial line with collapse curve). When the effective axial stress is not positive, the collapse SF slightly changes. A North Sea high-pressure, high-temperature well is also presented. The results indicate that the radial collapse SF is more realistic. In particular, it is most conservative in load cases with high effective axial stresses. The radial collapse SF has critical safety implications for load cases in which high effective axial stresses exist. Its software implementation can certainly assist well engineers with safer and more straightforward tubular design.