bluuebunny/crossref_metadata_2025_split huggingface dataset:
Permeability of Organic-rich Shale
Authors: Wasaki Asana , Akkutlu I. Yucel 
Published: October 2014

Abstract
Measured permeability of an organic-rich shale sample vary significantly with applied laboratory conditions, such as the confining pressure, temperature and the measurement fluid type. This indicates that the measured quantity is influenced by several mechanisms that add complexity to the measurement. The complexity is mainly due to stress dependence of the matrix permeability. Also, it is due to the fact that organic-rich shale holds significant volumes of fluids in sorbed (adsorbed, dissolved) states, sorption can also influence the permeability through its own storage and transport mechanisms. The stress-dependence and sorption effects on permeability could develop under the reservoir conditions and influence the production, although we currently do not have a predictive permeability model that considers their co-existence.
In this work this is accomplished by considering that the shale matrix consists of multiple continua with organic and inorganic pores. Stress-dependency of the permeability comes along with slit-shape inorganic pores, whereas the sorption effects are associated with nano-scale organic capillaries. A simple conceptual flow model with an apparent shale permeability is developed that couples the molecular transport effects of the sorbed phase with the stress-dependence of the inorganic matrix.
Sensitivity analysis on the new permeability model shows that the stress-dependence of the overall transport is significant at high pore pressure, when the effective stress is relatively low. Diffusive molecular transport of the sorbed phase becomes important as the stress gets larger and, hence, the inorganic pores close. The constructed apparent permeability versus pore pressure curves show the dominance of the molecular transport as permeability improvement characterized by appearance of a minimum permeability value at the intermediate values of the pressure. The new permeability model can be used easily in history-matching a well performance and optimizing its production.
Analysis and Interpretation of Haynesville Shale Subsurface Properties, Completion Variables, and Production Performance Using Ordination, a Multivariate Statistical Analysis Technique
Authors: Eburi S. , Jones S. , Houston T. , Bonelli J. 
Published: October 2014

Abstract
Shale plays have been considered statistical plays by many people in our industry. During the early years of the shale boom and even today, technical teams have looked to find correlations between variables to help explain well performance. Simple one-to-one correlations between individual variables and production performance in the Haynesville Shale appear to be weak because complex relationships may exist. Regression analysis techniques have assumptions and limitations, moreover, such techniques encounter difficulties when analyzing complex relationships. In this study, ordination, a multivariate analysis technique, was applied to subsurface and completion data to identify the variables that seem to influence well performance. The debate of nature versus nurture has often been applied to shale gas developments where some believe an effective well completion can make up for a poor shale reservoir rock quality while others believe a high quality shale reservoir rock is essential to obtain better producers despite an average well completion. The ordination technique successfully separated better performing wells from the less prolific wells and identified the variables that characterize the better producers. The same technique provided various optimum ranges for the different variables that could be used to identify core and non-core areas within the play. Our results suggest that (1) ordination is a viable method for multivariate statistical analysis in the Haynesville Shale; (2) subsurface variables are critical in obtaining better performing wells; and (3) completions variables are secondary but also very important.
Fault Reactivation as Mechanism of Early Water Production in Unconsolidated Sandstones Reservoirs
Authors: De Luca S. M. , Oliveira R. F. 
Published: October 2014

Abstract
There are several case studies showing that water injection apparently induces fault reactivation in oil and gas fields. However, there are not many publications in literature relating water production increasing with fault reactivation caused by depletion. The unexpected water production by fault reactivation related to depletion, promotes a huge impact on an oil field production, reducing the reservoir drainage efficiency and causing premature aging of operating plants, in some cases generating production bottlenecks due to produced water treatment limitations.
This paper presents parameters that influence fault reactivation mechanism and their importance in early water production.
In order to evaluate the phenomenon, a failure stability parameter that can be monitored during reservoir production time life is proposed and its behavior can be analyzed by comparison with produced water rate. The methodology is based on Mohr-Coulomb failure criterion using a simplified approach.
Basically, the method considers the change in the state of effective stresses, vertical and horizontal, during production and its ratio (σh/σv) called K0.The relationship of effective stress necessary for the Mohr circle touches the envelope of rupture for a given frictional angle, called K1, is also considered.
Thus, it is possible to observe the stress state changing as field production occurs using flow simulation. Fault reactivation analysis is obtained from the difference K0 minus K1, being K0 less or equal to K1, the condition at which depletion can induce faults in the reservoir. The method has been successfully applied on an unconsolidated sandstone reservoir from Campos Basin, explaining the evolution of anomalies in BSW (basic sediment and water) values that could not be predicted ignoring the theory of fault reactivation by depletion.
Recognizing that fault reactivations contribute to the unexpected water production is an important changing of paradigm in oil industry. The determination of the parameter of reactivation by depletion constitutes an important mark to indicate the right moment for beginning waterflooding. Many mature fields that present high Water Cut values, previously thought to have occurred due to rising of water-oil contact, may be revisited and restudied using the theory of fault reactivation by depletion. The approach can help to identify undrained-overlooked areas where infill drilling projects could be implemented, extending oil production and increasing oil recovery factors.
Probabilistic History Matching of Dual-Porosity and Dual-Permeability Korolev Model Using Three Discrete Fracture Models
Authors: Abdrakhmanova Aizada , Iskakov Elrad , King Gregory R. , Chaudhri Moon Mansoor , Liu Ning , Bateman Phil 
Published: October 2014

Abstract
Tengizchevroil (TCO) operates two giant carbonate oil fields, Tengiz and Korolev, located on the northeast shore of the Caspian Sea in Kazakhstan. Both fields are Middle Devonian to Upper Carboniferous isolated carbonate platforms. Since 1991, the fields have produced around 2B barrels of oil. As more geologic and dynamic data becomes available, an updated history match of the existing dynamic field models is required to provide more accurate simulation results for estimating reserves, optimizing production and assessing future field development opportunities.
In this paper, we present a case study on the use of brown-field design of experiments (DoE) on dual-porosity and dual-permeability Korolev field simulation model. Korolev is a highly fractured reservoir, and the physics of fluid movement is mainly controlled by the fracture network. In order to capture the uncertainty in the extent of fracture region at Korolev, discrete low, mid and high fracture models were created. The history matching process was split into three separate DoE studies, one for each of the fracture models, to obtain good quality proxies. After history matching the three fracture models separately, a combined proxy was created, and the final probabilistic P10, P50, P90 models were selected from the suite of all low, mid and high fracture extent models.
The history matching workflow consisted of selecting uncertainty parameters and ranges which honor geological data, identifying parameters having high impact on the history match quality, conducting brown-field DoE of historical and prediction periods, developing proxies for EUR objective functions and history match mis-match functions, and model selection. The majority of history matching effort was spent on the static well pressure match, followed by minimizing the modular dynamic test (MDT) pressures, production logging tool (PLT) profiles and water cut mis-match functions. Approaches in key areas which helped to improve the quality of Korolev history matched models will be discussed in detail.
The history matching workflow with low, mid and high fracture models described in this paper is believed to be superior to approaches that use a single fracture realization with fracture porosity, fracture permeability and sigma (fracture-matrix interaction term) history match modifications within the simulator. This is because the use of three fracture realizations allows for adjustment of intrinsic fracture properties (fracture density, aperture size, fracture extent, etc.). In addition, the use of three separate DoE studies allows for more accurate proxy model development.
A Workflow for Intelligent Data-Driven Analytics Software Development in Oil and Gas Industry
Authors: Dursun Serkan , Duman Kaan , Tuna Tayfun , Abbas Mamta , Ding James 
Published: October 2014

Abstract
A vast amount of oil and gas data has been collected over many years, which can be classified into different groups, including numerical, categorical, historical, real-time, structured, and unstructured. Therefore, analysis and visualization cannot be efficiently accomplished using traditional software platforms. Companies in the oil and gas industry need new technologies and processes to capture and transform data into strategic business intelligence to enhance exploration and production, improve refining and manufacturing efficiency, and optimize global operations while ensuring safety and environmental protection. With the advent of machine learning, data mining, optimization, and cloud computing, it is now possible to manage the large amount of data as never before.
Data-driven analytics, in this concept, offers new capabilities to the engineer in exploration and production to learn from data. Analyzing patterns in historical data and identifying the events in real-time data are two methods used for improving operations of activities and assets. The application of data-driven analytics in the oil and gas industry, therefore, potentially provides opportunities for greater insight and efficiency gains. The goal for data-driven analytics software is to provide the strategy required to optimize business outcomes and decision making while helping to minimize risk. The interaction between the users and the information through software tools results in a decision process that helps determine success of a project. The development of a data-driven software platform involves combining a variety of advanced machine learning and data-mining techniques, as well as data management strategies, to provide fit-for-purpose descriptive, predictive, and prescriptive models. Therefore, to be successful, the development team should have a deep understanding of both the underlying data science and the business model as well as software engineering principles.
This paper presents a workflow for an intelligent data-driven software platform development. The proposed workflow is composed of data management, data analytics and visualization, and predictive modeling. It covers best practices on database design, object-oriented design patterns and principles, predictive model building and deployment, user-interface development, and test-driven software development in an agile software development structure.
Economic Co-optimization of Oil Recovery and CO2 Sequestration
Authors: van't Veld Klaas , Wang Xiao , Alvarado Vladimir 
Published: October 2014

Abstract
Drawing on techniques from both petroleum engineering and economics, we analyze how oil fields that use CO2 injection to enhance oil recovery (CO2-EOR projects) are likely to respond to incentives that promote CO2 sequestration. Most engineering studies that have examined how oil-field operators should "co-optimize" oil recovery and CO2 sequestration use sophisticated reservoir models, but ad hoc economics. Conversely, a study by Leach et al. (2011), which to date is the only rigorous economic study of co-optimization, uses sophisticated economics, but an ad hoc reservoir model. Our study bridges the gap between these studies, by applying Leach et al.'s analysis to dynamic reservoir-simulation models of two different oil fields. We find that, even though Leach et al.'s assumptions about how a reservoir responds to CO2 injection are quite far off the mark quantitatively, their main findings hold up remarkably well qualitatively.
Experimental Study and Modeling of the Effect of Pore Size Distribution on Hydrocarbon Phase Behavior in Nanopores
Authors: Wang Lei , Neeves Keith B. , Yin Xiaolong , Ozkan Erdal 
Published: October 2014

Abstract
Pore sizes of many shale and tight oil and gas reservoirs are in the range of nanometers. In these pores, the phase behavior of hydrocarbon mixture is affected by the capillary pressure and the surface forces and is different from that characterized in PVT cells. Many existing phase behavior models use a single pore size. This research investigates the effect of a pore size distribution on the phase behavior of hydrocarbon mixtures.
Pure n-pentane and a ternary mixture of n-butane, i-butane, and n-octane were loaded into a nanofluidic device with microchannels and nano-channels to study phase transition due to evaporation. For n-pentane, evaporation in the nano-channels took place immediately after the liquid in the micro-channels completely evaporated. For the ternary mixture, however, evaporation in the micro-channels slowed down and did not progress into the nano-channels despite continuous heating, because evaporation in the micro-channels changed the composition of the remaining liquid.
A vapor-liquid equilibrium calculation procedure that considers the effect of capillary pressure, the sequence of phase change due to pore size distribution and the associated compositional change was developed and used to simulate depressurization of light oil and retrograde gas inside nanoporous media. The pore size distributions were characteristic of tight reservoirs and the fluid compositions were representative of typical reservoir fluids. Predictions of the model show that phase transition in porous medium with pore size distribution is a process that cannot be described by a single phase boundary, because the initial phase change alters the composition of the remaining fluid, which in turn suppresses the next phase change. For the oil, capillary pressure due to nanoconfinement increased the level of supersaturation and the critical gas saturation had a strong influence on the properties of produced fluids; for the retrograde gas, the effect of capillary pressure was insignificant due to the low interfacial tension. Despite the choice of fluids, calculations indicate that the smallest pores are probably always occupied by hydrocarbon liquid during depressurization. Experiments and modeling presented in this research provide tools to investigate and understand the effect of nanoconfinement on phase behavior, which assist the development of shale oil and gas condensate reservoirs.
Effect of Fluid Rheology and Reservoir Compressibility on Microseismicity During Hydraulic Fracturing
Authors: Hosseini S. Mehran , Neuhaus Carl W. , Aminzadeh Fred 
Published: October 2014

Abstract
Microseismic monitoring, as an essential surveillance tool of hydraulic stimulation, enables the industry to optimize the development of unconventional reservoirs ranging from wellbore azimuth and wellbore spacing to completion and treatment design as well as providing a means for quality control and treatment efficiency evaluation. In order to correctly interpret microseismic data, a clear understanding of the mechanisms driving the generation of microseismicity is crucial. It is widely accepted and supported by full moment tensor inversion that microseismic activity associated with subsurface fluid injection is mainly caused by shear failure. Microseismic events can be separated into two main categories: near-field events and far-field events. Near-field events are the microseismic events close to the induced fracture (i.e. hydraulic fracture); as a result, the stress shadow effect (i.e. stress perturbation around a hydraulic fracture) is the dominant factor controlling such events. Far-field events are events that are not occurring in the vicinity of the tensile hydraulic fracture, hence the stress shadow effect is not the dominant phenomenon, but they are mainly controlled by pore pressure diffusion. Recent seismicity-based permeability characterization models couple fluid flow and reservoir geomechanics in order to capture microseismic mechanisms and calculate bulk reservoir-scale permeability. The same fundamentals were used in a forward modeling sense; by knowing reservoir properties we were able to predict the spatio-temporal distribution of microseismic activity. The effects of three parameters, including reservoir fluid viscosity, fracture fluid viscosity, and total reservoir compressibility were studied. Furthermore, the microseismic response of the reservoir in conjunction with instantaneous pump attributes was investigated. A physics-based model showed that increasing the viscosity of both reservoir and hydraulic fracture fluid will decrease the local extent of the propagation of the microseismic cloud. Studying the effect of reservoir compressibility showed that an increase in total reservoir compressibility decreases the local extent of the microseismic cloud. Results of the analysis were then used to explain observations made in field data and described in the literature.
Methodology of Well Productivity Prediction from Analyzing Formation Tester Probe Transient Pressure Data in A Multilayer Reservoir Using A New Spherical Drawdown Analysis Method
Authors: Alali A. Z. , Sheng J. J. , Soliman M. Y. , Watson M. C. , Ma S. M. 
Published: October 2014

Abstract
Single probe formation testers are widely used tools in the oil industry for collecting fluid samples and measuring pressure transients to estimate initial formation pressure. Although in principle pressure transient data can be used to estimate formation permeability based on drawdown and buildup analyses, the permeablity estimated is rarely used in reality since these estimation methods are based on a single-layer or homogeneous reservoir which is rarely the case. There are many probe test data available in the industry. If we can use these data to estimate permeabilities from every location the tool is stopped and upscale these permeabilities to predict the well productivity index (PI) for the whole multilayer reservoir, then these pressure transient data become very valuable. This can save the cost of or completely replace expensive well tests.
In this paper, we study the effect of the distance of outer layers on permeability estimation in multilayer reservoir systems. We first investigate the effective radius of drawdown and buildup tests of a typical probe test in a multilayer reservoir. Then we define a dimensionless parameter to present this effective radius. This dimensionless parameter is a function of permeability and the distance from the probe to the outer layer. Within this effective radius, the permeability may be estimated within an acceptable engineering error.
A new method is proposed to estimate the well productivity of a multilayer reservoir from single probe tests. The estimated well productivity is compared with that from the conventional well tests using simulated data as well as actual test data. The productivities from our test analysis technique and other approaches are acceptably close. Therefore, this effective dimensionless radius may be used to guide the selection of test locations so that the test data can be used later to estimate the PI of a well.
Global Component Lumping for EOS Calculations
Authors: Alavian Sayyed Ahmad , Whitson Curtis Hays , Martinsen Sissel O. 
Published: October 2014

Abstract
To reduce CPU time in compositional reservoir simulations, a minimum number of components should be used in the equation of state (EOS) to describe the fluid phase and volumetric behavior. A "detailed" EOS model often contains from 20 to 40 components, with the first 10 components representing pure compounds H2S, CO2, N2, C1, C2, C3, i-C4, n-C4, i-C5, and n-C5. The remaining components represent a split of the heavier C6+ material in single-carbon-number (SCN) fractions such as C6, C7, C8 and C9, or groups of SCN fractions such as C10-C12, C13-C19, C20-C29, and C30+. Occasionally the light aromatics BTX (benzene, toluene, and xylene isomers) are also kept as separate components for process modeling. Today's typical laboratory compositional analysis provides 50-60 components, including isomers with carbon numbers 6 to 10, SCN fractions out to C35 and a residual C36+. This is in contrast to the 11-12 components (through C7+) reported in most commercial laboratory reports pre-1980.
A "pseudoized" EOS model might contain only 6-9 lumped components – e.g. lumping "similar" components such N2+C1, i-C4+n-C4+i-C5+n-C5, and some 3-5 C6+ fractions. The selection of which components to lump together is difficult because of the huge number of possible combinations. This paper describes a systematic, automated method1 to search a vast number of feasible pseudoized EOS models based on an initial, detailed EOS model.
The obvious application of pseudoized EOS models is compositional reservoir simulation, where run time is an important issue and fewer components may be important. The method we present is based on (1) quantifying the "quality of match" between a pseudoized EOS model and the detailed EOS model from which it is derived, and (2) systematically testing all plausible lumping combinations. The method allows for a set of constraints to be imposed on the lumping of components, such as (1) not lumping certain non-hydrocarbons (e.g. CO2), (2) forcing the first plus fraction to contain a specific carbon-number component (e.g. C6), and (3) the last component in the original EOS not being lumped with other heavy fractions (e.g. C30+).
The proposed pseudoization procedure is comprehensive, and founded in the ability of an EOS with fewer components to describe a wide range of phase and volumetric properties covering all of the relevant pressure-temperature-composition (p-T-z) space expected for a given reservoir development. The litmus test of quality is how well the pseudoized EOS compares with the detailed EOS model from which it is derived, an EOS that accurately describes all key measured laboratory PVT data. The method proposed will find an optimal pseudoized EOS model to describe all PVT data that are relevant to a particular reservoir development – e.g. depletion performance, immiscible and miscible gas injection, compositional variation, and surface processing.