bluuebunny/crossref_metadata_2025_split huggingface dataset:
Strategic Growth in Oil and Gas: A Capabilities Perspective
Authors: Capello Maria Angela , Dashti Qasem , Al-Khamees Waleed , Mohammed Moussa , De Sousa Maria J. 
Published: October 2018

Abstract
Kuwait Oil Company (KOC), one of the most important National Oil Companies (NOC) in the world, is growing in every sense, as the State of Kuwait is seeking to boost its oil production capacity by 2040, in line with the new approved strategy of Kuwait Petroleum Corporation. This growth into the future entails a shift from primary extraction to more complex settings, where new fields are produced simultaneously with others submitted to water flooding, EOR and Steam Injection, in what constitutes one of the biggest challenges ever faced by the company, due to the short timelines involved.
The company has a variety of technological partners that include International Oil Companies (IOC), to further support the 2040 strategy of the corporation. This strategic growth vision, not exempted of an increasing breath of challenges, is accompanied by a focused effort in building new technical capabilities in the personnel, and in gradually transforming the way the training plans shape the professional profiles and set the paths for ensuring the specialized skills and expertise that will be needed.
This paper describes how KOC is collaborating with an IOC, for uplifting and accelerating the training of its personnel, with the aim of enabling the availability of critical expertise needed to ensure production targets. Selected examples will detail how this cooperation NOC-IOC has been fruitful in enhancing competencies and training workflows. The teams involved from both sides of the spectrum experienced interesting challenges, and did not shy away from applying tactics of management of change to support the evolving talent development ecosystem.
An emphasis will be placed in describing why elements like cultural nuances, corporate legacy, communication, alignment and integration are considered key in the implementation of the training strategies. With a well-established KOC competency-based training scheme, and after several years of fruitful exchanges with the IOC, the uplifts achieved in talent development schemes have shaped a transformation journey, with specific results worth sharing.
Optimal 3D Reservoir Insight with a New Ultradeep-Reading Azimuthal LWD Resistivity Tool
Authors: Campbell Alan , Beeley Hugh , Bittar Michael , Walmsley Arthur , Hou Junsheng , Wu Hsu-Hsiang Mark 
Published: October 2018

Abstract
A new azimuthal electromagnetic (EM) logging-while-drilling (LWD) tool has been developed with multiple tilted antennas to measure three-dimensional (3D) electromagnetic fields. Multiple field trials successfully demonstrated the ultradeep detection range of more than 200 ft (60 m) with various transmitter-to-receiver spacings and operating frequencies, providing valuable geomapping insight for large-scale reservoir development. Additionally, this paper reveals the tool's capabilities in different geosteering applications, requiring different depth of detection (DOD) ranges for landing a well, optimizing well placement in thin reservoirs, and eliminating the need for a pilot hole.
This paper discusses in detail a new 3D finite-difference (FD) method to simulate realistic and complicated formation structures in three dimensions, enabling accurate formation interpretations and inversion of reservoir geology. Solving the scattered potential boundary value problem with the 3DFD numerical algorithm simulates the EM signals in this new LWD ultradeep application, and the modeling accuracy was benchmarked alongside in-house modeling codes and 3D commercial software. To accelerate the computation in the 3D modeling, sliding window, multicore parallel cloud computing, and decoupling between model pixel grid and FD simulation grid have been implemented for practical applications. Additionally, 3D modeling is used in the inversion to provide more accurate and complex reservoir determinations.
In addition to inversion, the tool provides 3D azimuthal multispacing, multifrequency geosignal, and resistivity measurements. Using the inversions and the 3D azimuthal images of the geosignal and resistivities enable improved reservoir understanding and geosteering decisions for the three dimensions. This paper describes two field trials from relatively thin to thick reservoirs to establish great and flexible geosteering performance because of multispacing, multifrequency measurements, and a robust signal and inversion process to optimize wellbore placements in the reservoir.
Evaluating the Impact of Multilayer Discrete Fracture Network in Hydraulic Fracture Geometry and Well Performance
Authors: Pankaj Piyush 
Published: October 2018

Abstract
Naturally fractured shale reservoirs are characterized by a complex 3D discrete fracture network (DFN). DFN density in different stratigraphic layers imposes variability in hydraulic fracture geometry as the hydraulic fracture propagates in the laminated shale reservoirs. The study presented in this paper aims to parametrically determine the hydraulic fracture geometry and consequent well production when varying densities of DFN interact with the propagation and growth of hydraulic fractures.
A 3D multilayer DFN (MDFN) is discretized in vertical grid elements distributed in layers of the formation. This is represented in a geocellular grid model with varying DFN sets in multiple layers. A new hydraulic fracture propagation model with stacked height has been utilized to determine the interaction of the hydraulic fractures with the DFN in all different layers. The vertical variation of the DFN causes more complex fracture propagation because it accounts for the fracture crossing criterion apart from the stress profile variation in layers and the stress shadow impact. Comparison of such complex hydraulic fracture geometry with the simplistic average 2D representation of the DFN reveals the geometry difference. This is further coupled to reservoir simulation in numerical engines to determine the difference in production performance. Finite element geomechanical simulations are made to predict microseismic events, which are then compared to the acquired microseismic data to validate the hydraulic fracture geometry.
Hydraulic fracture geometry was found to differ when an MDFN approach was taken as compared to simplistic single-layer DFN (SDFN) interaction. Production performance of the fracture geometry obtained by considering an MDFN was therefore more realistic. The simulated microseismic data also exhibited a much better match to the acquired microseismic data when an MDFN was used for modeling. Visual analytics tools for comparison of observed microseismic event distribution with predicted microseismic data in the model show errors ranging from 16% to 24% for different view representations. As a conclusion of this study, it was evident that wherever applicable, an MDFN approach should be used to determine the well completion performance. However, there is some scope for improving the modeling of hydraulic fracture interaction with natural fractures by incorporating the dip angle of the MDFN for computing the crossing criteria.
This study represents the first time a more-robust modeling of the interaction of hydraulic fractures with a 3D MDFN was achieved using complex fracture models to determine the impact on productivity. Finite-element-computed microseismic data allowed further validation. Operators can use the approach described in this study to predict their well performance more accurately in complex fracture reservoirs.
A Hybrid Data-Driven and Knowledge-Driven Methodology for Estimating the Effect of Completion Parameters on the Cumulative Production of Horizontal Wells
Authors: Guevara Jorge , Zadrozny Bianca , Buoro Alvaro , Lu Ligang , Tolle John , Limbeck Jan , Wu Mingqi , Hohl Defletf 
Published: October 2018

Abstract
We present a new methodology for modeling the cumulative oil and gas production of horizontal wells in a shale play given two types of well completion parameters: lateral length and proppant intensity, we also consider the location of the wells and the shut-in days of wells. We show experimental results evaluating the predictive accuracy of this methodology on hold-out data and compare it to standard data-driven estimation procedures. Our approach is based on the use of Generalized Additive Models (GAMs). The main advantage of using GAMs is that we can easily obtain effect plots that allow us to quantify the effect of each completion parameter and the location of the well on the production. Furthermore, it is possible to explicitly model the interaction between variables and impose shape constraints based on physical knowledge. We have implemented and tested this methodology in R and compared its predictive accuracy against a variety of standard data-driven modeling procedures available in the Caret package, using leave-one-out cross validation. We present experimental results using data from a shale play with 152 horizontal wells that have cumulative production for more than 12 months. Our experiments show that using GAMs in most cases leads to better predictive accuracy than the standard data-driven estimation procedures available in Caret, possibly because they allow us to explicitly model the interaction between input variables that are known to be are physically related and to impose shape constraints based on known physical relationships between input variables and target variables. We conclude that it is possible to accurately estimate the effect of the completion parameters on the cumulative production when we ensure that the models obey physical constraints, i.e., that the cumulative production is a monotonic increasing function of lateral length and of proppant intensity. We also obtain effect plots showing the "sweet-spot" effect, i.e., the relationship between the location (x, y) in the field and the cumulative production. The novelty of this hybrid data-driven and knowledge-driven methodology is to allow the quantification of the effect of completion parameters and location on well production through the combination, in a principled way, of data available from existing wells with prior/expert knowledge regarding known physical constraints.
Physics-Based Fluid Flow Modeling of Liquids-Rich Shale Reservoirs Using a 3D 3-Phase Multi-Porosity Numerical Simulation Model
Authors: Lopez Jimenez Bruno A. , Aguilera Roberto 
Published: October 2018

Abstract
Production from liquids-rich shale reservoirs in the United States and Canada has increased significantly during the past few years. However, a rigorous understanding of shale rocks and fluid flow through them is still limited and remains a challenge. Thus, the objective of this research is developing a 3D physics- based model for simulating fluid flow through these types of multi-porosity rocks. This is important given the recent spread of these types of reservoirs throughout the world.
Simulation of liquids-rich shale reservoirs is carried out with the construction of an original fully- implicit 3D multi-phase semi-compositional finite difference numerical formulation, which uses a multiple porosity approach as well as diffusion from solid kerogen. The multi-porosity system includes (1) adsorbed porosity, (2) organic porosity, (3) inorganic porosity, (4) natural fracture porosity, and (5) hydraulic fracture porosity. The numerical model is developed with capabilities to handle dissolved gas in the solid part of the organic matter, adsorption/desorption from the organic pore walls, viscous and non- Darcy flow mechanisms (slip flow and Knudsen diffusion), and stress-dependent properties of natural and hydraulic fractures.
Examples of simulated results are presented as cross-plots of pressure, production rates and cumulative production vs. time. These plots are utilized to show the contributions of free gas, adsorbed gas and dissolved gas on fluid production from liquids-rich shale reservoirs. Results indicate that both desorption and gas diffusion positively affect shales' performance. Simulation results demonstrate that not taking into account desorption and diffusion from solid kerogen leads to underestimating production from liquids-rich shale reservoirs. Furthermore, the simulation study shows that long periods of time are required for the effects of these two mechanisms to be manifested. This helps to explain why shales have been produced over long periods of time (several decades) like in the case of Devonian wells located in the Appalachian basin.
The type of 3D simulation model for multi-porosity liquids-rich shale reservoirs developed in this paper is not currently available in the literature. The approach implemented in this work provides a novel and important foundation for simulating complex shale reservoirs.
Application of Artificial Intelligence for Mechanistic Modeling and Probabilistic Forecasting of Hybrid Low Salinity Chemical Flooding
Authors: Dang Cuong , Nghiem Long , Fedutenko Eugene , Gorucu Emre , Yang Chaodong , Mirzabozorg Arash 
Published: October 2018

Abstract
After nearly thirty years of research and development, it is now widely agreed that Low Salinity Waterflooding (LSW) provides better oil recovery than High Salinity Waterflooding (HSW). Past studies also showed that there are significant advantages in combining LSW with other conventional EOR methods such as chemical flooding (polymer flooding and surfactant flooding) or miscible gas flooding to benefit from their synergies and to achieve higher oil recovery factor and project profit. This paper presents a study of Hybrid Low Salinity Chemical Flooding as a novel EOR approach with: (1) development of hybrid EOR concept from past decades; (2) implementation of an efficient modeling approach utilizing artificial intelligent technology for mechanistic modeling of these complex EOR processes; (3) systematic validation with laboratory data; and (4) uncertainty evaluation of LSW process at field scale.
The phase behavior of an oil-water-microemulsion system was modeled without the need of modeling type III microemulsion explicitly. The approach has been successfully applied to model both conventional Alkaline-Surfactant-Polymer (ASP) flooding and emerging EOR processes (LSW, Alkaline-CoSolvent-Polymer, and Low-Tension-Gas Flooding). The new development allows the mechanistic modeling of the benefits of combining LSW and chemical EOR. One of the main challenges for mechanistic modeling of these hybrid recovery processes is that several factors, e.g. polymer, surfactant, and salinity, can change the relative permeability simultaneously. To overcome this problem, Multilayer Neural Network (ML-NN) technique was applied to perform N-dimensional interpolation of relative permeability. The model was validated with coreflooding data and the effectiveness of hybrid processes were compared with conventional recovery methods.
The proposed model showed good agreements with different coreflooding experiments including HSW, LSW, and Low Salinity Surfactant flooding (LSS). This model efficiently captures the complex geochemistry, wettability alteration, microemulsion phase behavior, and the synergies occurring in these hybrid processes. Results indicated that LSS is an economically attractive hybrid EOR process since it increases the ultimate recovery factor compared to the conventional approaches and reduces surfactant retention. Bayesian workflow using ML-NN algorithm is efficient to capture the uncertainties in history matching and production forecasting of LSW.
As-Received Core Electrical Properties Tests for Determining Formation Factor FF and Resistivity Index RI
Authors: Newsham Kent , Hanna Ray , Lee Robert , Whitney Craig , Chemali Roland 
Published: October 2018

Abstract
We introduce a new core analysis workflow for determining Resistivity Index (RI), Formation Factor (FF), and other petrophysical properties, directly from an "as-received" (AR) set of core samples. Unlike common practices that require lengthy core cleaning and wettability restoration, the new workflow does not introduce external liquids or alter the wettability of the matrix. The new workflow starts with AR cores in the laboratory. Initial porosity and water saturation (Sw) are measured using nuclear magnetic resonance (NMR). We measure formation water resistivity (Rw) from a patented method based on resistivity dispersion. A two-electrode system provides the bulk resistivity (Rt) measurement. Sample de-saturation is by centrifuge at two drainage pressures. Sw and Rt measurement occurs at each de-saturation state. Sw measurement at each subsequent de-saturation step is by the gravimetric method. Regression analysis of the Rt and Sw data series yields Ro, the resistivity that would be expected for a 100% water-saturated sample, the Formation Factor (FF), the Resistivity Index (RI) and the Archie parameters of ‘m’ and ‘n’.
With the new workflow, cleaning, drying, and re-saturating the sample are no longer required. The risk of altering the initial wetting state of the sample is minimal. Another advantage is the reduction of lab analysis time from weeks to days. This is particularly valuable since FF and RI play an essential role in estimating hydrocarbon in place using Archie’s equation, and in providing insight into partial oil-wetting conditions.
Samples used in the method are from the Avalon and Wolfcamp formations. The method is also applicable to permeable, conventional rocks. The best estimation of Ro is from the paired Sw and Rt results measured at the AR state and after the first de-saturation step. As in the conventional method of measuring electric properties, a series of cross-plots provide a graphic solution for ‘m’ and ‘n’. A cross-plot of FF and porosity provides a graphical solution of the porosity exponent, ‘m’. A cross-plot of Sw and RI is the source for the solution of the saturation exponent, ‘n’. These graphic results compare well to results using a numerical solution. Finally, using the electric properties within the Archie equation, log based Sw values were found to be consistent with the measured Sw from core extraction.
CO2/Water Foams Stabilized with Cationic or Zwitterionic Surfactants at Temperatures up to 120 °C in High Salinity Brine
Authors: Da Chang , Elhag Armo , Jian Guoqing , Zhang Leilei , Alzobaidi Shehab , Zhang Xuan , Al Sumaiti Ali , Biswal Sibani , Hirasaki George , Johnston Keith 
Published: October 2018

Abstract
Stabilization of CO2 in water (C/W) foams with surfactants at high temperatures and high salinities is challenging, due to limited solubility of surfactants in aqueous phase, foamability and thermal stability. The apparent viscosities of C/W foams has been raised to up to 35 cP with viscoelastic aqueous phases formed with a diamine surfactant, C16-18N(CH3)C3N(CH3)2 (Duomeen TTM), or a zwitterionic surfactant, cetyl betaine, at 120 °C in 22% total-dissolved-solids (TDS) brine. Duomeen TTM is switchable from the nonionic (unprotonated amine) state, where it is soluble in CO2, to the cationic (protonated amine) state in an aqueous phase under pH ~6. Therefore, it may be injected in either the aqueous phase or the CO2 phase. The formation of viscoelastic phases with both surfactants lowers the minimum pressure gradient (MPG), and strengthens the lamella against drainage and Ostwald ripening by making the external aqueous phase more viscous, leading to stable foam even at very high foam quality. Both surfactants were shown to have excellent thermal stability and to form unstable emulsions when mixed with oil (dodecane). The core flood results showed that strong foam could be easily generated with both surfactants at a superficial velocity of 4 ft/day. The oil/water (O/W) partition coefficient of Duomeen TTM was very sensitive to pH, while that of cetyl betaine was constant over a wide range of pH. The ability to stabilize C/W foams at high temperature and salinity conditions with a single thermally stable surfactant is of great benefit to a wide range of applications including EOR, CO2 sequestration and hydraulic fracturing.
Consecutive Tracer Tests and Pressure Data Reveal Changes in Flow Regime Leading to Incremental Oil Production, Polymer Pilot, 8 TH Reservoir, Austria
Authors: Lüftenegger Markus , Clemens Torsten , Chiotoroiu Maria-Magdalena , Puls Christoph , Huseby Olaf 
Published: October 2018

Abstract
Polymer Flooding has been shown to increase oil production. The reason for increasing oil production is acceleration along flow paths but also flow diversion from higher permeability to lower permeability areas. Tracer tests performed in the 8 TH Reservoir in Austria prior, during and after polymer flooding show that the flow system dramatically changed. The connected volumes from injector to producer as well as the flow heterogeneity were influenced and substantial incremental oil produced. A number of tracer tests were performed in the pilot area of a polymer flood at various times. In addition, pressure data and polymer rheology was analyzed.
The tracer results were used to calculate flow pattern, dynamic Lorentz coefficient and connected volumes. Pressure data were used in combination with geomechanical modelling to investigate the injection regime (matrix or fractures). The interpretation of the data was combined with the determination of incremental oil production based on simulation. The tracer tests reveal the dramatic changes in flow patterns, connected volume changes by more than a factor of three occurred and the Lorentz coefficient indicating the heterogeneity of flow changed by more than a factor of two. The injection regime changed from injection under matrix conditions prior to polymer flooding to injection under fracturing conditions during polymer injection and back to injection in matrix conditions during chase water injection.
The reason for the changes in injection conditions is the near-wellbore viscoelastic rheology of the High Molecular Weight Polymers which were used. The growth of fractures leads to additional alteration of flow paths. The design of polymer flooding needs to take into account that flow paths are not only changed due to the reduction in water relative permeability resulting from polymer adsorbing to the rock and the increased viscosity of the injected fluid but also owing to changes in injection regime. The changes in injection regime might lead to early breakthrough of chase water as it might not flow along the same paths as the polymer solution.