bluuebunny/crossref_metadata_2025_split huggingface dataset:
Challenges in Deepwater Reservoir Characterization: From Well Log Interpretation and Well Testing to 3D Geocellular Modeling
Authors: Saikia Kalyan , Khan Waqar , Ramakrishnan Sandeep 
Published: September 2015

Abstract
With recent global success in many frontier deepwater prospects, these areas gained sudden attention (specifically presalt) amongst deepwater operators. As a result, exploration activities have increased many folds. Consequently, operators are trying to characterize these prospects as quickly as possible to maximize their return on investment (ROI). Such objectives can only be achieved if reservoir characterization of these blocks is performed with minimum error and uncertainty. To overcome these challenges, this paper presents an integrated approach wherein a combination of data aids constructing a representative initial three-dimensional (3D) geocellular model. This model is then fine-tuned and conditioned with the incorporation of well testing results from offset wells (if any). These preliminary models are created based on certain assumption and they need constant updates throughout the cycle of project to validate the theoretical assumptions combing detailed log interpretation and well testing data. A case study is presented to demonstrate the workflow and highlight the benefits of this technique.
Characterizing Reservoir Thermofacies by Using Distributed Temperature Sensing Measurements
Authors: Leone A. , Galli G. , Drago A. , Brown G. 
Published: September 2015

Abstract
A new distributed temperature sensing (DTS) interpretation method used in horizontal injection wells characterizes static facies and highlights independent features called thermofacies. This method allows for evaluating the intrinsic dynamic responses of reservoir layers using a time-lapse approach, taking into account the skin changes during the well's life and the possible activation of structural elements such as faults and fractures.
Based on DTS temperature analysis acquired during a well's shut-in period, this interpretation method provides two major results: the thermal performance indicator (TPI) and the thermofacies. The TPI adds a dynamic component to the static image of a well provided by openhole logs, even taking into account structural elements and inflow performance evaluation. When DTS inflow contributions are available, some independent facies (thermofacies) can be defined, showing the dynamic behavior of a well in a standalone mode.
The main advantage of this interpretation method is to provide a tool capable of driving field development to optimize the production while focusing on those log facies characterized by the dynamic injection performance; not only based on their static petrophysical evaluation.
A New Material Balance Equation for the Analysis of Liquid Rich Natural Gas Reservoirs
Authors: Zhang Miao , Ayala Luis F. 
Published: September 2015

Abstract
In this study, we analytically cross examine the consistency among available zero-dimensional material balance equations (MBEs) for liquid-rich gas equations and derive a new simple yet rigorous MBE starting from governing equations applicable to these systems. We propose a new zero-dimensional (i.e. tank) material balance equation that is directly applicable to the analysis of liquid-rich (wet and retrograde) gas reservoirs by expression of the equations in term of an equivalent gas molar density. Following model development, proposed model predictions of gas reservoir behavior with varying condensate content (lean, intermediate and rich) are investigated and critically compared to previous zero-dimensional models. All models are employed to predict reservoir performance given reservoir original-fluids-in-place and compared against benchmark examples created by numerical simulation. Actual field examples are also analyzed using existing and proposed models to test the ability of the proposed models to provide reliable reserve estimations using straight-line methods. The proposed density-based equation is proven to be straightforward to implement since is written in terms of density. This, in turn, allows it be directly expressed as an extension of the dry gas MBE, while not requiring the implementation of two-phase Z-factors.
Modeling of Compositional Variation with Depth for Five North Sea Reservoirs
Authors: Pedersen Karen Schou , Hjermstad Hans Petter 
Published: September 2015

Abstract
Compositional data for reservoir fluids sampled in various depths in five different North Sea reservoirs is presented and analyzed. All five reservoirs have a gas-oil contact and for each reservoir one composition exists from above and two or more from below the gas-oil contact. For all reservoirs the compositional gradient is higher than can be explained by gravity segregation alone. The highest compositional gradient is seen for a reservoir, which is rich in asphaltenes. The compositional variation with depth is modeled using the Haase model, which expresses the influence of a temperature gradient in terms of the specific enthalpies of each fluid component. The high compositional variation seen for the asphaltenic reservoir fluid suggests that the condensed aromatic compounds, which are the main constituents of asphaltenes, have a significantly higher absolute specific ideal gas enthalpy than paraffinic and naphthenic compounds.
Robust Optimization of Well Placement in Geologically Complex Reservoirs
Authors: Alpak Faruk O. , Jin Long , Ramirez Benjamin A. 
Published: September 2015

Abstract
Optimal decisions about well placement and control in geologically complex reservoirs can reduce subsurface risks associated with field development plans. Especially, in deepwater developments, economic constraints impose stringent limits on the number of wells that can be drilled without compromising economic profitability. Thus, optimal placement and operation of wells often have a significant impact on project rewards. Reservoir management decisions are made based on evaluations of subsurface models that integrate all available data by use of geomodeling and reservoir simulation. It is important to recognize that uncertainty is inherent to reservoir models due to limited data/knowledge about subsurface. A robust reservoir management strategy calls for rigorous techniques that account for stratigraphic and structural uncertainty in geologically complex deepwater reservoirs. A robust well-location optimization method is developed particularly suitable for such reservoirs. A steepest-ascent algorithm that takes advantage of distributed parallel computation constitutes the computational core of the optimization method. The method operates in parallel on a number of probable reservoir models and maximizes the expected value of ultimate recovery factor while delivering minimum variance over the model uncertainty range. A realistic synthetic test case is evaluated for demonstrating the concept. The case uses alternative geologic models of a lobate turbidite reservoir with complex stratigraphic architecture. The results validate the auspicious features of the robust well location optimization method.
A Modified HLD-NAC Equation of State to Predict Alkali-Surfactant-Oil-Brine Phase Behavior
Authors: Ghosh Soumyadeep , Johns Russell Taylor 
Published: September 2015

Abstract
Reservoir crudes often contain acidic components (primarily naphthenic acids), which undergo neutralization to form soaps in the presence of alkali. The generated soaps perform synergistically with injected synthetic surfactants to mobilize waterflood residual oil in what is termed alkali-surfactant-polymer (ASP) flooding. The two main advantages of using alkali in enhanced oil recovery (EOR) are to lower cost by injecting less expensive synthetic surfactant and to reduce adsorption of the surfactant on the mineral surfaces. The addition of alkali, however, complicates the measurement and prediction of the microemulsion phase behavior that forms with acidic crudes. For a robust chemical flood design, a comprehensive understanding of the microemulsion phase behavior in such processes is critical. Chemical flooding simulators currently employ Hand's method to fit a limited amount of measured data, but that approach likely does not adequately predict the phase behavior outside of the range of the measured data.
In this paper, we present a novel and practical alternative by employing a modified HLD-NAC equation of state that is predictive in nature. The HLD-NAC model uses the hydrophyllic-lypophillic difference (HLD) concept along with net and average curvature equations (NAC). The modified HLD-NAC model described by Ghosh and Johns (2014) to model surfactant-polymer (SP) phase behavior is extended here for ASP. We use an empirical equation to calculate the acid distribution coefficient from the molecular structure of the soap. Key HLD-NAC parameters like optimum salinities and optimum solubilization ratios are calculated from soap mole fraction weighted equations. The model is tuned to data from phase behavior experiments with real crudes to demonstrate the procedure. We also examine the ability of the new model to predict fish plots and activity charts that show the evolution of the three-phase region. The predictions of the model are in good agreement with measured data and all data is able to be matched.
A Multi-Objective Markov Chain Monte Carlo Approach for History Matching and Uncertainty Quantification
Authors: Olalotiti-Lawal Feyi , Datta-Gupta Akhil 
Published: September 2015

Abstract
We present a probabilistic approach for integrating multiple data into subsurface flow models. Our approach is based on a Bayesian framework whereby we exhaustively sample the multi-dimensional posterior distribution to define a Pareto front which represents the trade-off between multiple objectives during history matching. These objectives can be matching of water-cut, GOR, BHP and time-lapse seismic data. For field applications, these objectives do not necessarily move in tandem because of measurement errors and also interpretative nature of the seismic data.
Our proposed method is built on a Differential Evolution Markov Chain Monte Carlo (DEMC) algorithm in which multiple Markov Chains are run in parallel. First, a dominance relationship is established amongst multiple models. This is followed by construction of the posterior distribution based on a hypervolume measure. A unique aspect of our method is the proposal selection which is based on a random walk on two arbitrarily selected chains. This promotes effective mixing of the chains resulting in improved convergence.
We illustrate the algorithm using a nine-spot waterflood model whereby we use water-cut and bottomhole flowing pressure data to calibrate the permeability field. The permeability field is reparameterized using a previously proposed Grid Connectivity Transform (GCT) which is defined based on a decomposition of the grid Laplacian. The compression power of GCT allows us to reconstruct the permeability field with few parameters, thus significantly improving the computational efficiency of the MCMC approach. Next, we applied the method to the Brugge benchmark case involving 10 water injectors and 20 producers. For both cases, the algorithm provides an ensemble of models all constrained to the history data and defines a probabilistic Pareto front in the objective space. Several experimental runs were conducted to compare the effectiveness of the algorithm with NonDominated Sorting Genetic Algorithms (NSGA). Higher hypervolume was constantly measured using our algorithm which indicates that more optimal solutions were sampled. Our method provides a novel approach for subsurface model calibration and uncertainty quantification using MCMC in which the communication between parallel Markov chains enhances adequate mixing. This significantly improves the convergence without loss in sampling quality.
We demonstrate that the proposed approach can efficiently integrate seemingly conflicting data into reservoir models while quantifying uncertainties. Besides reservoir model calibration, we expect that the algorithm will find application in probabilistic production optimization problems as well.
Rock Typing of Tight Gas Sands: A Case Study in Lance and Mesaverde Formations From Jonah Field
Authors: Aliyev Elshan , Saidian Milad , Prasad Manika 
Published: September 2015

Abstract
The Jonah field is one of the biggest tight gas sand fields in the Green River basin. Production profiles from its deeper sections show high liquid hydrocarbons close to the Pinedale anticline, especially in Mesaverde and Lance formations. To assess the potential of condensate production, new approaches for rock classification are needed to differentiate between discontinuous sandstone layers and the interbedded siltstones. A gamma ray cut off of 75 API has been defined to distinguish between sandstone and siltstone in logs and core samples in Lance and Mesaverde Formations. Significant variation of porosity and permeability occurs within the sandstone zones. This variation warrants new rock typing approaches. We present rock typing for tight sandstones and siltstones with an understanding of petrophysical properties such as pore structure, porosity, permeability, and cementation.
We studied 94 samples from the Mesaverde and Lance Formations with lithologies varying from clean sandstone to shale. X-ray diffraction (XRD) mineralogy, mercury injection capillary pressure (MICP), helium porosity and permeability were measured for all samples. NMR transverse relaxation times (T2) at 2 MHz were also measured for 10 water saturated samples. Nitrogen adsorption was performed on 11 samples from Mesaverde formation to determine pore volume and pore size distribution.
MICP data are used to subdivide rocks into three groups based on pore throat size distribution: reservoir sandstones, non-reservoir sandstone and siltstone/mudstone. Dominant pore throat size for reservoir and non-reservoir sandstones are 400 and 100 nm, respectively. In order to apply pore throat size rock typing to downhole measurements, NMR pore size classification is used to identify formations. Pore size from NMR demonstrated equivalent behavior to pore throat size from MICP. The logarithmic mean values of T2 transverse relaxation times for reservoir, non-reservoir sandstone and siltstone/mudstone are 22.2 ms, 3.4 ms and 0.29 ms, respectively. Clear separation of reservoir sandstone, non-reservoir sandstone and siltstone is seen based on pressure dependency of ultrasonic compressional and shear velocities during initial pressure loading. Reservoir sandstone demonstrates the highest compressibility. In addition, siltstone and mudstone were separated based on log differential pore volume distribution from N2 adsorption data.
Based on pore size distribution data, four main rock types are identified in Lance and Mesaverde formations in Jonah field. Rock typing based on gamma ray and porosity logs can be considered as rock classification of end members. To capture transitional behavior in between end members, pore size distribution is needed in logging application. Since NMR T2 distribution show similar spectra to MICP throat size distribution, the rock typing technique can be applied using NMR log data. Separation of mudstone from siltstone can be used for identification of shale end points in log data.
Modeling Point Bars Using a Grid Transformation Scheme
Authors: Li Henry , Srinivasan Sanjay 
Published: September 2015

Abstract
Point bars, formed from meandering rivers, exhibit distinct heterogeneities. Modeling these heterogeneities is essential because of the presence of mud/silt layers that impede steam chamber rise in Steam-Assisted Gravity Drainage (SAGD). Previous point-bar models rely heavily on outcrops for geology. Such models may have difficulty extending to subsurface reservoirs where geological data is minimal. This paper details the modeling of point bars using geological trends and well data to supplement geostatistical simulation.
The modeling processes in this paper capture the internal geometry of the accretions layers as well as geological trends. Curvilinear grids are constructed between major erosional surfaces where a grid transformation scheme is used to transform the curvilinear coordinates into orthogonal coordinates for geostatistical simulation. The grid transformation allows flexibility of performing statistical simulation in rectangular coordinates while honoring the curvilinear geometry of the point bar. An entire point bar model contains a series of accreting curvilinear grids. Geological modeling is done independently for each grid. The geology captures key trends that make up the heterogeneities in the model.
The point bar model created is based on a modern point bar in the Brazos River. Data from thirty-three wells are used in creating the reservoir model. Several distinct trends are observed. There is an upward decrease in sediment size in the point bar. An overall fining downstream trend is observed in the vertical slices of the point bar. Heterolithic bedding is observed where frequent layers of silt extend from the top to near the base analogous to outcrops. Mud and silt dominate the upward regions of the point bar while conglomerate and cobble are mainly present at the base.
This paper presents a reservoir modeling process that properly define point bar geology using only well data and geological trends. Without reliance on outcrops, modeling of point bars can be applied to subsurface reservoirs. A detailed geologic model provides greater insight to the heterogeneities seen in point bar reservoirs. Well defined heterogeneities in point bar models is critical to any SAGD simulation.
Incorporating Quality Assurance in a Competency and Employee Development Program
Authors: Leuro Jorge , Kruger Todd 
Published: September 2015

Abstract
Organizations within the oil and gas industry have invested heavily in embedding competency and employee programs into their business framework and corporate cultures, expecting that these will be instrumental to delivering superior business results by building and maintaining workforce capability.
To execute this strategy, individuals should be engaged with the program by reliably adopting the principles and applying the processes. However, with respect to accountability, individuals will choose to either adopt behaviors that drive compliance or complacency for themselves and others. The inherent challenges, therefore, are recognizing that complacency can exist, identifying nonconformance, correcting undesired behaviors, and continuously improving the belief system of the organization.
This paper discusses how a global full-spectrum oil and gas service company is ensuring that individuals adopt desired behaviors by using a company-wide approach to help ensure quality within the competency program. Quality assurance practices have been incorporated in the competency program as a unanimous request from the organization to safeguard the quality of the business intelligence possessed about the competence of the workforce.
The company continues to build confidence in the program's ability to provide high-quality repeatable evidence that supports the development (competence) of employees, the demonstration of the competence of the workforce, and the delivery of superior business results.
A Numerical Analysis of the Features of the Stress Disturbance during Pulsating Hydraulic Fracturing in Coal Seams
Authors: Lu Peiqing , Li Gensheng , Huang Zhongwei , Li Xiaojiang , Tian Shouceng , Song Xianzhi , Sheng Mao 
Published: September 2015

Abstract
Pulsating hydraulic fracturing (PHF) is useful in the development of methane from geologically complex coal seams in China because it can relieve pressure in a great range and increase the permeability of coal seams. Current research studies on PHF mainly focus on coal rock damage via experimental methods; however, to the best of our knowledge, none of these studies consider stress disturbances, which significantly impact the stimulation effect. In this paper, a numerical model is proposed to investigate the features of stress disturbances under loads of PHF in coal seams.
Based on field data, a stress-response numerical model applies a high-order, staggered-grid, finite-difference scheme to investigate the stress disturbance features of coal seams. This model combines the linear slip deformation model and the preloading method to describe the cleats and confining pressure, respectively. Moreover, the implementation of an infinite formation makes use of the perfectly matched layer technique. The model has been verified for the static and dynamic theoretical calculation cases, which can be regarded as sufficiently precise for analyzing the stress disturbance.
The numerical simulation results show several unique stress behaviors with PHF. First, the stress disturbance is characterized by an extensively altered scope and regional stress concentration, and the effective stress disturbance zone can be much larger compared with conventional fracturing. Second, the PHF disturbance effects can be improved by increasing the source frequency and amplitude. Third, the strong compressibility (i.e., the high elastic Young's modulus and low Poisson's ratio) of coal rock is beneficial for stress propagation and disturbances in improving the fracturing effect. Fourth, the crack density (0−0.2) has little impact on the PHF stress disturbance effect because of the diffraction of the stress wave.
The proposed model is able to describe the static and dynamic stress response in an infinite fractured formation with confining pressure. The model clarifies the propagation and distribution of stress, and it can help us understand the fracture mechanism and suitable coal seam conditions for PHF from the perspective of the formation stress disturbance. Based on this model, the results can improve the optimization of the technical parameters and the investigation of the stimulation mechanics of PHF in coal seams.