bluuebunny/crossref_metadata_2025_split huggingface dataset:
Probabilistic and Deterministic Methods: Applicability in Unconventional Reservoirs
Authors: Coll C. , Elliott S. 
Published: June 2013

Abstract
Unconventional resources are pervasive throughout large areas and are not affected by hydrodynamic forces. In contrast with conventional reservoirs, the discovery risk is typically low with reservoir boundaries typically extending beyond the limits of the acreage holding. When estimating reserves and resources, the major uncertainty in unconventional reservoirs tends to be around the local reservoir properties that control well production potential and ultimate recovery. Very high areal variability of factors such as permeability, well deliverability, saturation state, rock mechanical properties exists. Accordingly, field appraisal is continuous as the field is developed to help understanding reservoir heterogeneity dictates the initial production from the wells and decline rates. Technological advances help optimizing the mechanical efficiency of well operations improving the economic viability of these resources through increases in well rates and ultimate recoverable volumes accessed by each well as demonstrated by the successful implementation of new fraccing technologies in shale gas reservoirs.
The new SEC1 rules adopted in 2009 allow the application of probabilistic methods for reserves and resources estimations. In unconventional reservoirs different stages of maturity exist as described by SPE PRMS2, COGEH3 and SPEE4 Monograph 3 guidelines. The COGEH guidelines are based on deterministic methods whereas SPEE Monograph 3 guidelines are mainly focused on probabilistic methodologies to use for reporting reserves in resource plays (CSG, Shale, Tight Gas/Oil and Basin-centered Gas Systems) particularly how to estimate proved undeveloped reserves in areas where enough drilling and production exist. COGEH3 (volume 3) provides valuable guidance on deterministic estimation of reserves and resources for coal bed methane (CBM) and Bitumen/SAGD consistent with the definitions provided in COGEH Volumes 1 and 2. In November 2011 the new Guidelines for Application of the Petroleum Resources Management System were published by the SPE5. These new guidelines used the 2001 original guidelines as the starting point updating significantly two new areas: "Estimation of Petroleum Resources Using Deterministic Procedures" and Unconventional Resources. The 2011 SPE guidelines cover more extensively unconventional reservoirs describing the reservoir characteristics, extraction and processing methods, assessment methods, commercial and classification issues for heavy oil, bitumen, tight gas formations, coalbed methane, shale gas, oil shale and gas hydrates. This paper provides some guidance on best practices on the applicability of deterministic and probabilistic methods to estimate reserves and resources for unconventional reservoirs based on the maturity of the resource play and existing industry guidelines.
An Adaptive Evolutionary Algorithm for History-Matching
Authors: Abdollahzadeh Asaad , Christie Mike , Corne David , Davies Brian , Elliott Michael 
Published: June 2013

Abstract
Efficient history matching of highly uncertain reservoir models is important in many applications of the reservoir engineering area, such as reservoir management, production prediction, and development optimisation. History matching has commonly been done manually by a tedious trial and error approach in which global and local adjustments are done to the model properties until a best model is obtained which honours production response of the reservoir model. With recent advances in computer science and hardware, assisted history matching techniques can improve the quality of the match within less time and effort.
Stochastic evolutionary algorithms are popular methods in history matching and have been widely used to explore and search the global parameter search space and find multiple good fitting models. They are quite easy to implement, and if well applied, able to be globally convergent even in complex problems such as history matching. General critiques of these algorithms include high computational demands and demand for the tuning of the algorithm’s control parameters. Adaptation methods for EAs adjust algorithm’s control parameters during the evolution to improve the search quality and accelerate the convergence by guiding search toward appropriate regions of the search space. This insures a balance between exploration and exploitation properties of the algorithm.
In this paper, we introduce a novel adaptive scheme for EAs, which intelligently adapts the control parameter that affects the diversity of the population. The adapted control parameter is number of selected to generated solutions in each generation, which controls the balance between exploration and exploitation. We apply proposed algorithm to optimisation problem of two test functions and history matching problem of a well-known synthetic reservoir simulation model. Our results show that, compared with the original EAs, adaptive EA is able to find better spread of fitting models with better convergence to the minimum misfit, thus more effective and efficient history matching is achieved.
Reservoir Scale permeability Prediction Using Advanced Modeling Techniques - Application to Turbidite Reservoirs
Authors: Baggio Sylvie , Sirju Clive , Adaszkiewicz Monika , Pritchard Tim , Lovell Mike , Davies Sarah 
Published: June 2013

Abstract
Log derived permeability averages in homogeneous clastic reservoirs most often matches the reservoir scale permeability determined from well testing. However, when it comes to heterolithic, anisotropic reservoirs such as observed in successions interpreted as turbidites, there can be significant differences between reservoir scale estimates and traditional techniques such as arithmetic and geometric averaging. This mismatch is often overlooked, although it can be critical when it comes to history matching production data. The fundamental flaw of the log-only approach is that it assumes permeability to be isotropic, and therefore ignores the three-dimensional nature of permeability. In heterogeneous clastic reservoirs, the anisotropies originating from various small scale sedimentary structures may have an influence on fluid flow at larger scales, impacting the observed reservoir-scale permeability.
This paper demonstrates how data acquired from wireline logs and cores from very heterogeneous successions like turbidites may be used to consistently predict the reservoir scale permeability. The workflow consists in processing and integrating the data into a lamina-scale near-wellbore model containing structural as well as petrophysical properties; a flow-based upscaling method is then applied to provide a geologically consistent input into a single-well model, from which a typical well test scenario will be simulated; the resulting pressure transient is then analyzed to determine a simulated permeability-thickness product.
Presented in this paper is an application to two wells intersecting a turbidite reservoir. This shows that the permeability obtained from this workflow is closer to the well test permeability when compared to log derived estimates from traditional averaging techniques.
This method can be applied to obtain valid reservoir-scale permeability values that can then be compared to the actual well test result. However, it can also be applied in cases where the value of information processes prove well testing to be uneconomical; then, a global reservoir permeability value can still be obtained using the workflow described.
Seismic History Matching with Saturation Indicators Combined with Multiple Objective Function Optimization
Authors: Stephen Karl D. 
Published: June 2013

Abstract
Time-lapse seismic data offers additional information about the reservoir which can be used in history matching. Observed seismic data is often available as a relative measure of pressure and saturation changes and it can be difficult to compare directly to modelled data. Usually we need to calibrate the observed data or otherwise scale it for use in the misfit function. An alternative is to use a qualitative measure but this means that the production and seismic misfits can no longer be compared quantitatively. In this paper to solve this we examine the use of multiple objective functions to find better models that match the data.
We use a qualitative measure of the change of acoustic impedance in both the model and observed data domains to determine how much change takes places during production. Then we compare regions of change as a measure of similarity. With production data we use a conventional sum of squares misfit. A stochastic algorithm is used to find the best solutions. We modify the neighbourhood algorithm so that we accept models that will best fit either seismic or production misfits in a multi-objective fashion.
We demonstrate the approach on a synthetic model where the answer is known. We compare the results using a more conventional sum of squares misfit with one which uses multiple objective functions. We show that both the NA finds a better set of solutions that match both sets of observed datasets. We quantify the speed at which this occurs.
History matching with time-lapse seismic data adds additional constraints to conventional approaches with just production data. Models better reflect observations and are more reliable for forecasting. This leads to better decision making and planning in reservoir development.
Observed Spatial Statistics of Permeability and the Effects on Fluid Flow: are we Getting it Right?
Authors: Xiao-Qian Lin , Stephen Karl , Steele Richard 
Published: June 2013

Abstract
In geocellular modeling, the variogram is used to represent the spatial correlation of rock properties. Horizontal variograms are often assumed rather than measured due to the scarcity of subsurface data. Wells are drilled kilometers apart while reservoir heterogeneity occurs at much smaller scales. The use of outcrop data as analogues has therefore been widely practiced by geoscientists to supplement the estimation of horizontal variograms. Through research of reported observations from outcrop, this paper will reveal that the strategy used to define sampling intervals and data density in published outcrop studies can vary significantly with implications for modelling and simulation.
The impact on experimental variogram calculation has not been given sufficient attention and there is large variation, and hence uncertainty, on any variogram data used in modelling. We examine the choice of sampling interval and sufficiency on the derived variogram properties via a synthetic study. We also investigate the uncertainty of variograms used in modelling on flow simulation.
A number of models have been created to represent the range of variability in the spatial correlation of permeability reported in outcrop studies. The models were sampled with various intervals and amounts of data to create experimental variograms. We find that insufficient density of data can lead to variograms that may be misinterpreted leading to an incorrect choice of variogram range in subsequent modelling. From flow simulations we show how this is critical and that over- or under-estimation of the range can vary recovery factors by ten per cent.
Reservoir modelling and simulation is important for field management. A proper representation of the subsurface rock properties such as permeability is crucial so that accurate predictions and forecasts can be made. The most appropriate choice of semi-variogram parameters will therefore have a strong impact on reliability of models leading to more effective decision making.
High-Resolution Reservoir Modeling Using Image Fusion Technique in History Matching Problems
Authors: Sayyafzadeh Mohammad , Haghighi Manouchehr 
Published: June 2013

Abstract
History matching is an inseparable part of reservoir modelling in which flow performance data are incorporated into geological model to reduce uncertainty in decision making. Although, thus far numerous studies have been published in order to augment the quality of history matching, a comprehensive approach is not being taken toward it, and development in different steps of history matching, including reparameterization and optimization, is looked for. Reparameterization is used to reduce the number of decision variables and subsequently the degree of calibration freedom in order to expedite history matching procedure. But, it always brings reparameterization error into the problem.
In this study, an inventive algorithm is introduced in which reparameterization is unnecessary and accordingly the corresponding error is eliminated. In this algorithm, entire uncertain parameters including porosity and permeability distributions can directly be adjusted. Image fusion technique is widely used in image processing problems. Image fusion is a technique for combining a number of images into a single image to afford a more informative image. In this study, image fusion technique is applied in history matching problems. In the designed algorithm, different models intelligently and stochastically are merged based on their corresponding fitness values to produce a fitting model. It is a repetitive approach until stopping criteria are met. Hence, it performs similar to evolutionary algorithms while image fusion works as a mating operator.
In order to assess the proposed algorithm, the outcomes of history matching using this algorithm on a synthetic model and also PUNQ-S3 reservoir model are compared with a number of history matching approaches. The outcomes demonstrate an improvement over the applied approaches in terms of the quality of the history matched models.
The proposed approach is coped with a significantly larger number of variables, and accordingly high resolution history matched models are achieved. Also, the algorithm has an acceptable speed of convergence even in comparison with variable metric algorithms.
Computational Methodology to Study Heterogeneities in Petroleum Reservoirs
Authors: Cevolani J. T. , Mostafa A. E. , Vital E. A. , de Oliveira L. Costa , da Fonseca L. Goliatt , Sousa M. Costa 
Published: June 2013

Abstract
Characterization of hydrocarbon reservoirs is strategically important to define the productivity of oil and/or gas fields. It involves many challenges such as appropriate identification, classification and interpretation of diagenetic processes that directly affect the quality of the reservoirs. Proper studies demand integration and analysis of very large amounts of data, usually presenting high-dimensional feature spaces. Current methods have many manual steps leading to a limited exploration of the data. These challenges are being intensified due to the need of knowledge and time dedication from experts. We developed a novel methodology that combines established techniques, such as Principle Component Analysis (PCA), clustering methods, parallel coordinates and scatter plots, with features such as dynamic (magic) lenses – filter and shadow lenses –, axes reordering and color maps, to automatically perform reservoir characterization in order to assist the identification, validation and interpretation of petrofacies. Petrofacies is a set of petrographic characteristics of microscopic order which allow the analyst to understand the diagenetic processes, aiding in the evaluation of the potential for hydrocarbon storage in the reservoir. We have applied our methodology on several databases from different sedimentary basins – Espirito Santo and Parana basins (Brazil), Talara Basin (Peru) and Niger Delta Basin (Nigeria). We conclude that our method allows the analyst to gain insights about the entire database in a manner that is faster than the analysis using a manual method. It also allows validation of the results because it is a powerful tool that can qualitatively and quantitatively support the analyst in the identification, interpretation and validation of petrofacies. This new methodology can optimize data analysis of similar databases, accelerating the analysis and reducing the committed work by the experts.
Some Case Studies of Temperature and Pressure Transient Analysis in Horizontal, Multi-zone, Intelligent Wells
Authors: Muradov Khafiz , Davies David 
Published: June 2013

Abstract
Accurate measurement of real-time temperature and pressure across the well completion interval(s) has become standard in the modern production and injection wells. The data is used for routine production/injection monitoring, well stimulation, enhanced recovery operations, etc. During the last decade both the number of wells permanently equipped with pressure and temperature gauges as well as the accuracy and sensitivity of the sensors has increased dramatically. Large volumes of data are now produced that is often not "mined" for all the embedded information. Moreover, technology developments are exacerbating this data management problem with the availability of ever more reliable sensors at a lower cost, let alone the development of more advanced sensors capable of measuring additional properties of the flowing fluid.
The ambition of the Digital Oilfield is to provide continuous optimisation of field production by real-time, well control and monitoring. However, the actual level of use made of these large volumes of data is still low.
Downhole temperature measurements are able to provide extensive information for supporting well production diagnostics and optimisation. Recent publications have demonstrated that transient temperature analysis (TTA) has a high information potential with the unique advantage of being able to quantify zonal in- or out-flow. This derives from TTA being less affected by wellbore storage effects while at the same time providing a distinctive "build-up" or "draw-down" response to the performance of an individual reservoir layer. This latter has proven to be a highly productive source of zonal well production information, either as a stand-alone data source or in combination with pressure measurements, across the production interval(s).
This paper will present examples of pressure and temperature transient analysis of downhole data from a real, multi-zone, intelligent well. It will discuss the qualitative aspects of sandface and wellbore pressure and temperature response along with their quantitative analysis to provide zonal, multi-phase flow rate allocation.
The paper will provide novel insights into downhole data analysis for a horizontal well producing from multiple intervals. They can be applied to quantitatively interpret either discrete or distributed temperature data, both with and without accompanying pressure measurements. Application of the workflow proposed here can immediately increase the "Added value" derived from intelligent well technology.
Spatial Constraints for the Stochastic Modeling of Fault Networks in the Presence of Large Structural Uncertainties
Authors: Julio Charline , Caumon Guillaume 
Published: June 2013

Abstract
Faults greatly impact the heterogeneities, the fluid flow and the geomechanical behavior of hydrocarbon reservoirs. However, significant uncertainty affects fault location, extent and connectivity due to limited quality and lack of subsurface data. In contrast to the standard practice of reservoir modeling which tends to represent only one deterministic fault network, we propose to sample structural uncertainty about fault networks (both geometrical and topological) by simulating a set of possible models all constrained by subsurface data. The conditioning of the faults may be done from fault sticks interpreted from 2D or 3D seismic surveys. Fault sticks correspond to one interpretation and must be used carefully especially when the uncertainties on the fault position are high. The objective of this paper is to introduce a new method to constrain the stochastic simulation of seismic-scale faults by negative constraints which condition the non-occurrence of fault.
We propose to divide the simulation space into two subspaces: (1) a volume where the simulation of faults is allowed and (2) a volume where the occurrence of faults is excluded. These two volumes are separated by a closed surface, named envelope, used to constrain the position of faults. During stochastic fault simulation, we optimize the geometrical fault parameters (position of the center, dip, strike) so as to minimize a misfit function. The efficiency of this conditioning approach is tested on synthetic data sets.
The described simulation method is applied in the cases of large-scale and high uncertainties which can be characterized by the envelope delimiting the exclusion zones. The algorithm simulates faults which honor both statistical input parameters and exclusion constraints. The high uncertainties lead to the simulation of various fault networks with different geometries and topologies.
In the petroleum industry, the stochastic approach is widely used to manage the facies and petrophysical property uncertainties, but it is rarely applied to fault network modeling. The presented optimization algorithm belongs to data conditioning methods for stochastic fault simulation, with the distinctive feature to be adapted to dense and continuous data. The fault-exclusion zone is adequate to represent the continuous structural information recorded by the seismic data.
Produced Water Chemistry History Matching in the Janice Field
Authors: Vazquez O. , Young C. , Demyanov V. , Arnold D. , Fisher A. , MacMillan A. , Christie Mike 
Published: June 2013

Abstract
Produced Water Chemistry data (PWC) is the main source of information to monitor scale precipitation in oil field operations. Chloride concentration is used in order to evaluate the seawater fraction of the total produced water per producing well and is included as an extra history matching constraint to revaluate a good conventionally history matched reservoir model for the Janice field. Generally PWC is not included in conventional history matching and this approach shows the value of considering the nature of the seawater injection front and the associated brine mixing between the distinctive formation water and injected seawater.
Adding the extra constraint resulted in the re-conceptualization of the reservoir geology between a key injector and two producers. The transmissibility of a shale layer is locally modified within a range of geologically consistent values. Also, a major lineament is identified which is interpreted as a NW-SE trending fault, whereby the zero transmissibility of a secondary shale in the Middle Fulmar is locally adjusted to allow cross-flow. Both uncertainties are consistent with the complex faulting known to exist in the region of the targeted wells. Other uncertainties that were carried forward to the assisted history matching phase included: water allocation to the major seawater injectors; thermal fracture orientation of injectors and the vertical and horizontal permeability ratio (kv/kh) of the Fulmar formation.
Finally, a Stochastic Particle Swarm Optimization (PSO) algorithm is used to generate an ensemble of history matched (HM) models using seawater fraction as an extra constraint in the misfit definition. Use of addition data in history matching has improved the original good history matched solution. Field Oil Production Rate is interpreted as improved over a key period and although no obvious improvement was observed in Field Water Production Rate, Seawater fraction in a number of wells was improved.