bluuebunny/crossref_metadata_2025_split huggingface dataset:
Topological Data Analysis to Solve Big Data Problem in Reservoir Engineering: Application to Inverted 4D Seismic Data
Authors: Alfaleh Abdulhamed , Wang Yuhe , Yan Bicheng , Killough John , Song Hongqing , Wei Chenji 
Published: September 2015

Abstract
Data analysis is one of the most important topics in any industry. In petroleum engineering, the large, complex, and multi-dimensional reservoir data sets (big data) presents a challenge for engineers to study the masses of unstructured information and make decisions. A new approach to analyze complex data is called Topological Data Analysis (TDA) which aims to extract meaningful information from such data. TDA relies on the concept that complex data has shapes where shape has meanings. It analyzes the shape of complex data, identifying clusters and their statistical significance. The objective of this paper is to introduce TDA to reservoir engineering using an example of inverted 4D seismic data for studying reservoir connectivity and compartmentalization.
In this paper, we introduce the principles of TDA and discuss its potential in reservoir engineering, which could allow identification of reservoir engineering data behavior, recognition of new opportunities, detection of anomalies and events, and minimizing uncertainties. The TDA procedures are introduced using inverted 4D seismic data set to study reservoir connectivity and compartmentalization. The process to generate and process the data set is explained. Similarity distance function and lenses are defined and used to create TDA graphs for feature identification and analysis.
It is shown that TDA is able to predict the compartmentaliztion of the reservoir models with various process configurations. Variance normalized Euclidean and topological neighborhood function are used successfully to compartmentalize the reservoir model. Using normalized input dataset, correlation and principle component analysis also create similar compartments. The success of TDA in discovering meaningful patterns is attributed to the similarity distance function representing the objective of study, one or more lenses exposing the data, and the right combination of input data, similarity distance function and lenses.
A promising big data analysis method, TDA, is introduced to reservoir engineering application with principles, procedures and examples. It has been shown that TDA can automatically discover critical intelligence within the 4D seismic data set for studying reservoir connectivity and compartmentalization, which are essential to the accuracy of forecasts and development plans, the validity of reservoir simulation, and the success of performance diagnostics and optimization.
Operational Risk: Stepping Beyond Bow-Ties
Authors: Fraser D. , Braun J. , Cunningham M. , Marple B. , Moore D. D. , Sas-Jaworsky A. , Wilson J. 
Published: September 2015

Abstract
This paper presents the Multiple Physical Barrier (MPB) approach to operational (or process) risk that was developed by Argonne National Laboratory with the help of key collaborators from the upstream oil and gas industry.
This approach begins with a comparison to the “bow-tie” technique that is commonly used in the industry for identifying operational (or process) risk. Bow-ties identify a variety of different types of barriers and help communicate safety principles that link causal factors and subsequent actions to a specific event – such as a loss of well control event. Bow-tie barriers commonly include equipment, processes, plans, methods, and sometimes people, with their know-how and experience.
However, by narrowing the focus to physical barriers (e.g. the source of operational risk) as is consistent with the NORSOK D-010 Standard, and by developing “success paths” that enable each barrier to perform its “safety function,” the MPB approach is demonstrated to step further toward a systematic approach to operational risk management. Additionally, this approach can be seen to help guide operational process design as well as lifecycle management.
To demonstrate the versatility of the MPB approach, several applications have been selected that highlight how operational risk can be systematically identified and communicated in different offshore applications: Drilling, including an operational risk comparison between conventional drilling and Managed Pressure Drilling; Completions, installing and de-installing production packers, a barrier that highlights the life-cycle aspect of the MPB approach; and Coiled Tubing Workovers, a risk based design diagram for ensuring that barriers are in place to ensure a successful operation.
By making a clear distinction between physical barriers and success path components (including human actions, processes, and plans), the MPB approach adds systematic clarification for understanding and characterizing risks in a direct manner. The MPB approach also enables the identification of chains of causality and builds important steps toward enabling risk quantification. As such, the MPB approach provides a direct and systematic approach for assessing and communicating risk, as well as helping operations to be successful.
Production Analysis Of One Shale Gas Reservoir In China
Authors: Pang Wei , Wu Qiong , He Ying , Du Juan , Zhang Tongyi , Ehlig-Economides Christine A. 
Published: September 2015

Abstract
China is the third country gaining shale gas breakthrough in the world after the United States and Canada. Fuling shale gas play is the most successful shale gas reservoir by far in China with average absolute open flow (AOF) capacity of 400 thousand cubic meters per well and the amount reaches 8 million cubic meters in one year for some wells. However, production rate and pressure transient analysis show quite different flow regimes which may mean that the wells are producing quite differently and stimulating treatment have distinct performance.
In this paper, 20 wells in the shale gas reservoir in China are analyzed with production rate and pressure transient data using rate normalized pressure (RNP) and rate normalized pressure derivative (RNP') method. The 20 wells log-log interpretation plots are drawn to diagnose the flow regimes and flow regime sequence. In the log-log plots, nearly half of the wells see unit slop, which indicate boundary dominated flow within 1 to 2 years, so the stimulated reservoir volume (SRV) is very small. From production rate data, most producing wells see interference when their neighborhood wells are hydraulic fracturing. Interference imposes great effect on log-log interpretation plots, so interpretation comparison are made with and without using rate and pressure transient data after interference. Because of interference, liquid loading may be a problem for some wells. External source of energy supplying for the shale gas layer is researched by rate and pressure transient interpretation modeling because some wells see external pressure supply.
Results show that Fuling shale gas reservoir has unique production performance with production rate keeping nearly constant within 2 years for some wells. Fuling Phase I shale gas wells exhibit 4 different flow regimes with relevant interpretation models categorized by homogenous formation with boundary, dual porosity formation, pressure support, severe liquid producing. Interference with adjacent wells during hydraulic fracturing may occur and cause great effect on rate and pressure transient data interpretation.
To the author's knowledge, this is the first time that comprehensively describes shale gas production performance detail in China. 4 different flow regimes with different formation interpretation models within one reservoir block are rarely reported.
East Vacuum Grayburg San Andres Unit, 30 Years of CO2 Flooding: Accomplishments, Challenges and Opportunities
Authors: Moffitt Paul , Pecore Doug , Trees Mark , Salts Greg 
Published: September 2015

Abstract
The East Vacuum Grayburg San Andres Unit (EVGSAU) began CO2 injection in 1985 which is still continuing today. Performance of CO2 injection has been very favorable in the main pay zone with oil recoveries exceeding 45 percent overall in the unit. Technical challenges remain in maintaining and increasing oil recovery but future opportunities also exist such as CO2 flooding the transition/residual oil zone and improving overall volumetric sweep efficiency.
Waterflooding in EVGSAU began in 1980 to pressure up the reservoir in preparation for CO2 flooding. CO2 injection began in 1985 with a 2:1 water: alternating gas (WAG) ratio to help provide conformance control. Overall oil recovery from the main portion of the reservoir in the CO2 flooding area alone is above 55 % OOIP. Approximately 12.5 % OOIP recovery has been due to CO2 flooding. This paper will detail some of the challenges experienced during CO2 flooding which include maintaining reservoir pressure above minimum miscibility pressure, maintaining high injection rates in WAG injection wells, and handling large volumes of produced gas. During the early 2000's, 19 laterals were drilled out of existing vertical wells which assisted the unit in increasing oil production rates and reducing gas coning.
Future opportunities include expansion of the current gas plant to handle the increasing gas-oil ratio of produced oil as the CO2 flood continues to mature, CO2 flooding the transition/residual oil zone, infill drilling in the main pay zone to increase areal sweep efficiency and polymer gel treatments to address poor vertical conformance. A foam treatment is also being planned for EVGSAU.
CO2 flooding in the EVGSAU has been impacted during its history by changing oil and CO2 prices and many technical challenges. Despite these challenges, oil recovery by CO2 flooding has been favorable and further steps are being taken to increase long-term oil recovery.
Development of a General Likelihood Maximization Method for Robust Parameter Estimation from Pressure Transient Data
Authors: Bandyopadhyay Parag , Horne Roland N. 
Published: September 2015

Abstract
Conventional parameter estimation methods used in pressure transient analysis assume specific analytical probability distributions of error and use corresponding likelihood functions to estimate the key parameters such as reservoir permeability, wellbore storage, skin and distance to reservoir boundary. The most commonly used method is that of least squares, which assumes that measurement errors follow a Gaussian distribution. Lack of a priori information regarding the nature of these measurement errors have made the ordinary least squares method (OLS) the most common method for parameter estimation.
In this study, on examination of multiple field data sets, the assumption of Gaussian errors was found to be weak. The use of OLS in such cases leads to biased estimates of reservoir parameters. A general method based on the framework of likelihood maximization that is inherent to parameter estimation was developed. This method does not require a prior assumption regarding the nature of the distribution of the underlying errors in the data. A hybrid variation was developed to improve the convergence properties of this method. The accuracy and convergence properties of this method were compared to that of OLS for field and synthetic datasets and found to be superior to OLS for arbitrary error distributions, while still maintaining accuracy in cases with truly Gaussian errors.
The Reservoir Technical Limits Approach Applied to Maximising Recovery from Volumetric and Aquifer-drive Gas Fields
Authors: Alane Abdelmadjid , Lumsden Peter J. , Smalley P. Craig , Hallam Richard , Salino Peter A. , Wells Steven J. , Primmer Tim J. 
Published: September 2015

Abstract
Maximising recovery of hydrocarbons from oil and gas fields represents responsible asset management and is extremely valuable both to the operator and the host country. Doing this successfully involves a complex combination of technical, commercial, organizational and human factors. This was addressed by developing the Reservoir Technical Limits (RTL™) process; the process and its application to oil fields was described in a 2009 SPE paper (109555). The present paper describes subsequent progress in developing RTL™, including a description of a new gas efficiency factor framework for use in volumetric and aquifer-drive reservoirs.
Transient Flowing-Fluid Temperature Modeling in Oil Reservoirs for Flow Associated with Large Drawdowns
Authors: Chevarunotai N. , Hasan A. R. , Kabir C. S. 
Published: September 2015

Abstract
Modern downhole temperature measurements indicate that bottomhole fluid temperature could be significantly higher or lower than the original reservoir temperature, especially in reservoirs where high pressure drawdown is expected during production. This recent finding contradicts the isothermal assumption originally made for routine caluculations. In a high-pressure drawdown environment, the Joule-Thomson (J-T) phenomenon plays an important role in fluid temperature alteration in the reservoir.
This paper presents a robust analytical model to estimate the flowing-fluid temperature distribution in a reservoir that accounts for the J-T heating or cooling effect. All significant heat-transfer mechanisms for fluid flow in the reservoir, including heat transfer due to convection, J-T phenomenon, and heat transfer from over- and under-burden formations, are incorporated in this study. The proposed model is successfully validated with results from a rigorous numerical simulator using field data.
Modeling Method of Point Bar Internal Architecture of Meandering River Reservoir Based on Meander Migration Process Inversion Algorithm and Virtual Geo-surfaces Automatic Fitting Technology
Authors: Shu Xiao , Hu Yong , Jin Baoqiang , Dong Rencheng , Zhou Haiyan , Wang Jijun 
Published: September 2015

Abstract
This paper developed a new modeling method of the point bar internal architecture of meandering river reservoir based on a new meander migration process inversion algorithm and virtual geo-surfaces automatic fitting technology. Firstly, we proposed a new inversion simulation algorithm of meander migration process based on the river hydrodynamics which can backward simulate the migration centerlines of channel at each time step starting from the centerline of the terminal abandoned channel. Considering the fact that lateral accretion interlayers have the same strike with the ancient channel where they draped, through extracting restored channel centerlines at very high density combined with 3D geometry characterization, the 3D virtual geo-surfaces of lateral accretion interlayers inside the point bar were built. After examining the extent of match-up between each virtual geo-surfaces and well data in 3D space, the virtual geo-surfaces which don't conform to well data were removed and the virtual geo-surfaces which conform to both well data and predefined rules were retained as the final geo-surfaces model of real lateral accretion interlayers. In order to test the reliability of this method, we applied this method to study the reservoir architecture of the west region of NmI3 depositional unit of M oilfield in Bohai Bay Basin, East China.
M oilfield is one of the biggest oilfield in Bohai Bay Basin, East China. A total of 140 wells penetrated the NmI3 depositional unit. The average well spacing is about 400m. Constrained by the well spacing and the size of the channel and the point bar, we only characterize the 4th order architecture unit by means of interwell 3D correlation. And the lateral accretion interlayers model was built using the inversion simulation method.
Based on the internal architecture model of the point bar, we explained the big difference of fluid production rate between 2 prodution wells which are in the same point bar and have roughly the same distance to the injection water well. The results show that the the injected water flows along the strike of lateral accretion interlayers, suggesting an advantageous flow path was formed inside the point bar. So the relative spatial position between wells and lateral accretion interlayers is a key factor that influences production dynamic. This rule can be used to guide well placement and waterflooding optimization in point bar reservoir.
Dimensionality-Dependent Foam Rheological Properties: How to Go From Linear to Radial Geometry for Foam Modeling and Simulation?
Authors: Lee Woochan , Lee Seungjun , Kam Seung Ihl 
Published: September 2015

Abstract
Numerous laboratory and field tests reveal that foam can effectively control gas mobility and improve sweep efficiency, if correctly designed. It is believed that there is a significant gap between small laboratory-scale experiments and large field-scale tests because of two main reasons: (i) typical laboratory flow tests are conducted in linear systems, while field-scale foam EOR processes are performed in radial (or spherical partly) systems; and (ii) through the complicated in-situ lamella creation/coalescence mechanisms and non-Newtonian behavior, foam rheology depends on the geometry and dimensionality. As a result, it is still an open question how to translate laboratory-measured data to field-scale treatments.
For the first time, this study investigates how foam rheological properties change depending on the dimensionality and how such dimensionality-dependent properties are affected by different flowing conditions, by using mechanistic foam fractional flow analysis. Complex foam flow characteristics such as three foam states (weak-foam, strong-foam, and intermediate states) and two steady-state strong-foam regimes (high-quality regime and low-quality regime) lie in the heart of this analysis.
The calculation results from a small radial and spherical system showed that (i) for strong foams in the low-quality regime injected, foam mobility decreased (or mobility reduction factor increased) significantly with distance which improved sweep efficiency; (ii) for strong foams in the high-quality regime, the situation became more complicated – near the well foam mobility decreased, but away from the well foam mobility increased with distance, which eventually gave lower sweep efficiency; and (iii) for weak foams injected, foam mobility increased with distance which lowered sweep efficiency. The results implied that the use of fixed value of mobility reduction factor, which is a common practice in field-scale reservoir simulations, might lead to a significant error. When the method was applied to a larger scale, it was shown that strong foams could propagate deeper into the reservoir at higher injection rate, higher injection pressure, and at lower injection foam quality. Foam propagation distance was very sensitive to these injection conditions for strong foams in the high-quality regime, but much less sensitive for strong foams in the low-quality regime.
Water Content Effects on Dynamic Elastic Properties of Organic-rich Shale
Authors: Lai B.T. , Li H. , Zhang J.L. , Jacobi D. , Georgi D. 
Published: September 2015

Abstract
Acoustic velocity measurements are an important non-destructive way to investigate dynamic rock mechanical properties. Water content and bedding-plane-induced anisotropy are reported to significantly impact the acoustic velocities of siliciclastic sandstones and laminated carbonates. This relationship in organic-rich shales however is not well understood and has yet to be investigated. The mechanical properties of organic-rich shales are affected by changes in water content, laminations, total organic content (TOC), and microstructures. In particular, kerogen density that accompanies changes in the composition of the TOC during maturity can significantly influence the acoustic responses within source rocks.
To understand how these variables influence acoustic responses in organic shales, two sets of cores from the Eagle Ford shale were investigated: one set cut parallel to bedding and the other perpendicular to bedding. Textures of the samples from each set were characterized using CT scanning. NMR was used to measure the water content, and XRD to analyze the mineralogy. Scanning Electron Microscope (SEM) was also used to characterize the microstucture. Acoustic velocity measurements were then made on each set at various confining pressures using the ultrasonic pulse transmission technique.
The results show that confining pressure, water content and laminations have significant impact on both compressional (P-wave) and shear wave (S-wave) velocity. Both velocities increase as confining pressure increases. Velocities measured from cores cut parallel to bedding are on average 20% higher than those cut perpendicular to bedding. Increasing water content decreases both velocities. The impact of water content on shear velocity was found to be significant compared to the response with compressional velocity. As a result, the water content was found to lower both Young's modulus and shear modulus, which is opposite to the reported results in conventional reservoir lithology. In addition, both P- and S- wave velocities show a linear decrease as TOC increases, and also they both decrease with increasing of clay content. The mechanisms that lead to water content alternation of rock mechanical properties might be a combined result of the clay-water interaction, the chemical reaction, and the capillary pressure changes.
Machine Learning Applied to Multiwell Test Analysis and Flow Rate Reconstruction
Authors: Tian Chuan , Horne Roland N. 
Published: September 2015

Abstract
Permanent downhole gauges (PDGs) can provide a continuous record of flow rate and pressure, which provides us rich information about the reservoir and makes PDG data a valuable source for reservoir analysis. In previous work, it has been shown that kernel ridge regression based machine learning is a promising tool to interpret pressure transients from a single PDG. Kernel ridge regression denoises and deconvolves the pressure signal efficiently and recovers the full reservoir behaviors. In this work, the machine learning framework was extended to two applications: multiwell testing and flow rate reconstruction.
The multiwell testing was formulated into the machine learning algorithm using a feature-coefficient-target model. The features were nonlinear functions of flow rate histories of all the wells. For each well, the features and the pressure target of this well were used to train its coefficients, which implicitly contain the information about the reservoir model and well interactions. The reservoir model can then be revealed by predicting the pressure corresponding to a simple rate history with the trained model. The multiwell machine learning model was demonstrated to be useful in several different ways, including artificial interference testing and reservoir pressure prediction at various well locations based on flow rate data only.
Flow rate reconstruction aims at estimating any missing flow rate history by using available pressure history. This is a very useful capability in practical applications in which individual well rates are not recorded continuously. A set of new features were developed as functions of pressure to model the flow rate. Coupled with kernel ridge regression, the developed features were tested on both synthetic and real data sets and demonstrated high prediction accuracy. The success of the rate reconstruction modeling also illustrates the flexibility of machine learning to different kinds of modeling, by adapting features and targets.
The models for both applications maintained the advantages of the machine learning based single well pressure interpretation in terms of the accuracy of prediction, computational efficiency and tolerance to noise. This work further demonstrates machine learning as a promising technique for PDG data analysis.