Relevant bibliographies by topics / Probabilistic Analysis of Max 3-Satisfiability

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Probabilistic Analysis of Max 3-Satisfiability'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Probabilistic Analysis of Max 3-Satisfiability"

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Chao, Ming-Te, and John Franco. "Probabilistic Analysis of Two Heuristics for the 3-Satisfiability Problem." SIAM Journal on Computing 15, no. 4 (1986): 1106–18. http://dx.doi.org/10.1137/0215080.

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Levray, Amélie, Salem Benferhat, and Karim Tabia. "Possibilistic Networks: Computational Analysis of MAP and MPE Inference." International Journal on Artificial Intelligence Tools 29, no. 03n04 (2020): 2060005. http://dx.doi.org/10.1142/s0218213020600052.

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Possibilistic graphical models are powerful modeling and reasoning tools for uncertain information based on possibility theory. In this paper, we provide an analysis of computational complexity of MAP and MPE queries for possibilistic networks. MAP queries stand for maximum a posteriori explanation while MPE ones stand for most plausible explanation. We show that the decision problems of answering MAP and MPE queries in both min-based and product-based possibilistic networks is NP-complete. Definitely, such results represent an advantage of possibilistic graphical models over probabilistic one
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Jacobs, G., and F. Theunissen. "Predicting spatial activity patterns in a neural map from a probabilistic atlas of 3-D reconstructed neurons." Proceedings, annual meeting, Electron Microscopy Society of America 53 (August 13, 1995): 812–13. http://dx.doi.org/10.1017/s0424820100140439.

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In order to understand how the algorithms underlying neural computation are implemented within any neural system, it is necessary to understand details of the anatomy, physiology and global organization of the neurons from which the system is constructed. Information is represented in neural systems by patterns of activity that vary in both their spatial extent and in the time domain. One of the great challenges to microscopists is to devise methods for imaging these patterns of activity and to correlate them with the underlying neuroanatomy and physiology. We have addressed this problem by us
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Boyle, Eileen M., Brian A. Walker, Christopher P. Wardell, et al. "Mutational Patterns and Copy Number Changes at Diagnosis Are a Powerful Tool to Predict Outcome: Result of the Sequencing Study of 463 Newly Diagnosed Myeloma Trial Patients." Blood 124, no. 21 (2014): 637. http://dx.doi.org/10.1182/blood.v124.21.637.637.

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Abstract Background: The main genetic features of myeloma identified so far have been the presence of balanced translocations at the immunoglobulin heavy chain (IGH) region and copy number abnormalities. Novel methodologies such as massively parallel sequencing have begun to describe the pattern of tumour acquired mutations detected at presentation but their biological and clinical relevance has not yet been fully established. Methods: Whole exome sequencing was performed on 463 presentation patients enrolled into the large UK, phase III, open label, Myeloma XI trial. DNA was extracted from ge
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Wan, Yue, Jingxiong Zhang, Wenjing Yang, and Yunwei Tang. "Refining Land-Cover Maps Based on Probabilistic Re-Classification in CCA Ordination Space." Remote Sensing 12, no. 18 (2020): 2954. http://dx.doi.org/10.3390/rs12182954.

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Due to spatial inhomogeneity of land-cover types and spectral confusions among them, land-cover maps suffer from misclassification errors. While much research has focused on improving image classification by re-processing source images with more advanced algorithms and/or using images of finer resolution, there is rarely any systematic work on re-processing existing maps to increase their accuracy. We propose refining existing maps to achieve accuracy gains by exploring and utilizing relationships between reference data, which are often already available or can be collected, and map data. For
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Albanwan, H., and R. Qin. "ENHANCEMENT OF DEPTH MAP BY FUSION USING ADAPTIVE AND SEMANTIC-GUIDED SPATIOTEMPORAL FILTERING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 227–32. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-227-2020.

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Abstract. Extracting detailed geometric information about a scene relies on the quality of the depth maps (e.g. Digital Elevation Surfaces, DSM) to enhance the performance of 3D model reconstruction. Elevation information from LiDAR is often expensive and hard to obtain. The most common approach to generate depth maps is through multi-view stereo (MVS) methods (e.g. dense stereo image matching). The quality of single depth maps, however, is often prone to noise, outliers, and missing data points due to the quality of the acquired image pairs. A reference multi-view image pair must be noise-fre
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Palma, Giuseppe, Serena Monti, Roberto Pacelli, et al. "Radiation Pneumonitis in Thoracic Cancer Patients: Multi-Center Voxel-Based Analysis." Cancers 13, no. 14 (2021): 3553. http://dx.doi.org/10.3390/cancers13143553.

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This study investigates the dose–response patterns associated with radiation pneumonitis (RP) in patients treated for thoracic malignancies with different radiation modalities. To this end, voxel-based analysis (VBA) empowered by a novel strategy for the characterization of spatial properties of dose maps was applied. Data from 382 lung cancer and mediastinal lymphoma patients from three institutions treated with different radiation therapy (RT) techniques were analyzed. Each planning CT and biologically effective dose map (α/β = 3 Gy) was spatially normalized on a common anatomical reference.
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Jiménez, A., A. M. Posadas, and J. M. Marfil. "A probabilistic seismic hazard model based on cellular automata and information theory." Nonlinear Processes in Geophysics 12, no. 3 (2005): 381–96. http://dx.doi.org/10.5194/npg-12-381-2005.

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Abstract. We try to obtain a spatio-temporal model of earthquakes occurrence based on Information Theory and Cellular Automata (CA). The CA supply useful models for many investigations in natural sciences; here, it have been used to establish temporal relations between the seismic events occurring in neighbouring parts of the crust. The catalogue used is divided into time intervals and the region into cells, which are declared active or inactive by means of a certain energy release criterion (four criteria have been tested). A pattern of active and inactive cells which evolves over time is giv
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Kim, Hyun Il, and Kun Yeun Han. "Inundation Map Prediction with Rainfall Return Period and Machine Learning." Water 12, no. 6 (2020): 1552. http://dx.doi.org/10.3390/w12061552.

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To date, various methods of flood prediction using numerical analysis or machine learning have been studied. However, a methodology for simultaneously predicting the rainfall return period and an inundation map for observed rainfall has not been presented. Simultaneous prediction of the return period and inundation map would be a useful technique for responding to floods in real-time and could provide an expected inundation area by return period. In this study, return period estimation for observed rainfall was performed via PNN (probabilistic neural network). SVR (support vector regression) a
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Emmi, Philip C., and Carl A. Horton. "A GIS-Based Assessment of Earthquake Property Damage and Casualty Risk: Salt Lake County, Utah." Earthquake Spectra 9, no. 1 (1993): 11–33. http://dx.doi.org/10.1193/1.1585703.

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This paper offers a probabilistic assessment of expected property damage and casualty risk due to the earthquake ground shaking hazard affecting Salt Lake County, Utah (population = 725,600). Salt Lake County is bisected by a segment of the Wasatch Fault. It is also at risk from twenty-one other nearby fault segments. Findings are based on (1) a microzonation of the earthquake ground shaking hazard, (2) an inventory of buildings by value, structural frame type and use, (3) earthquake damage functions defining the performance of buildings as a function of ground shaking intensity, (4) data on t
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Dissertations / Theses on the topic "Probabilistic Analysis of Max 3-Satisfiability"

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Aytemiz, Tevfik. "A Probabilistic Study of 3-SATISFIABILITY." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/28202.

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Discrete optimization problems are defined by a finite set of solutions together with an objective function value assigned to each solution. Local search algorithms provide useful tools for addressing a wide variety of intractable discrete optimization problems. Each such algorithm offers a distinct set of rules to intelligently exploit the solution space with the hope of finding an optimal/near optimal solution using a reasonable amount of computing time. This research studies and analyses randomly generated instances of 3-SATISFIABILITY to gain insights into the structure of the underlyin
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Book chapters on the topic "Probabilistic Analysis of Max 3-Satisfiability"

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Kaporis, Alexis C., Lefteris M. Kirousis, and Efthimios G. Lalas. "The Probabilistic Analysis of a Greedy Satisfiability Algorithm." In Algorithms — ESA 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45749-6_51.

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Abate, Alessandro, Mirco Giacobbe, and Diptarko Roy. "Learning Probabilistic Termination Proofs." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_1.

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AbstractWe present the first machine learning approach to the termination analysis of probabilistic programs. Ranking supermartingales (RSMs) prove that probabilistic programs halt, in expectation, within a finite number of steps. While previously RSMs were directly synthesised from source code, our method learns them from sampled execution traces. We introduce the neural ranking supermartingale: we let a neural network fit an RSM over execution traces and then we verify it over the source code using satisfiability modulo theories (SMT); if the latter step produces a counterexample, we generate from it new sample traces and repeat learning in a counterexample-guided inductive synthesis loop, until the SMT solver confirms the validity of the RSM. The result is thus a sound witness of probabilistic termination. Our learning strategy is agnostic to the source code and its verification counterpart supports the widest range of probabilistic single-loop programs that any existing tool can handle to date. We demonstrate the efficacy of our method over a range of benchmarks that include linear and polynomial programs with discrete, continuous, state-dependent, multi-variate, hierarchical distributions, and distributions with undefined moments.
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Hu, Tao, Sinan Mutlu, and Oswald Lanz. "Multicamera People Tracking Using a Locus-based Probabilistic Occupancy Map." In Image Analysis and Processing – ICIAP 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41184-7_70.

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Yacoub, Méziane, Dominique Frayssinet, Fouad Badran, and Sylvie Thiria. "Clustering and Classification Based on Expert Knowledge Propagation Using a Probabilistic Self-Organizing Map: Application to Geophysics." In Data Analysis. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58250-9_6.

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Oikonomou, Konstantinos, and Ioannis Stavrakakis. "Throughput Analysis of a Probabilistic Topology-Unaware TDMA MAC Policy for Ad-hoc Networks." In Quality for All. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45188-4_18.

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Kinne, Marko, Ronald Schneider, and Sebastian Thöns. "Reconstructing Stress Resultants in Wind Turbine Towers Based on Strain Measurements." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_18.

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AbstractSupport structures of offshore wind turbines are subject to cyclic stresses generated by different time-variant random loadings such as wind, waves, and currents in combination with the excitation by the rotor. In the design phase, the cyclic demand on wind turbine support structure is calculated and forecasted with semi or fully probabilistic engineering models. In some cases, additional cyclic stresses may be induced by construction deviations, unbalanced rotor masses and structural dynamic phenomena such as, for example, the Sommerfeld effect. Both, the significant uncertainties in the design and a validation of absence of unforeseen adverse dynamic phenomena necessitate the employment of measurement systems on the support structures. The quality of the measurements of the cyclic demand on the support structures depends on (a) the precision of the measurement system consisting of sensors, amplifier and data normalization and (b) algorithms for analyzing and converting data to structural health information. This paper presents the probabilistic modelling and analysis of uncertainties in strain measurements performed for the purposes of reconstructing stress resultants in wind turbine towers. It is shown how the uncertainties in the strain measurements affect the uncertainty in the individual components of the reconstructed forces and moments. The analysis identifies the components of the vector of stress resultants that can be reconstructed with sufficient precision.
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Hausmann, Daniel, and Lutz Schröder. "Quasipolynomial Computation of Nested Fixpoints." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_3.

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AbstractIt is well-known that the winning region of a parity game with n nodes and k priorities can be computed as a k-nested fixpoint of a suitable function; straightforward computation of this nested fixpoint requires $$\mathcal {O}(n^{\frac{k}{2}})$$ O ( n k 2 ) iterations of the function. Calude et al.’s recent quasipolynomial-time parity game solving algorithm essentially shows how to compute the same fixpoint in only quasipolynomially many iterations by reducing parity games to quasipolynomially sized safety games. Universal graphs have been used to modularize this transformation of parity games to equivalent safety games that are obtained by combining the original game with a universal graph. We show that this approach naturally generalizes to the computation of solutions of systems of any fixpoint equations over finite lattices; hence, the solution of fixpoint equation systems can be computed by quasipolynomially many iterations of the equations. We present applications to modal fixpoint logics and games beyond relational semantics. For instance, the model checking problems for the energy $$\mu $$ μ -calculus, finite latticed $$\mu $$ μ -calculi, and the graded and the (two-valued) probabilistic $$\mu $$ μ -calculus – with numbers coded in binary – can be solved via nested fixpoints of functions that differ substantially from the function for parity games but still can be computed in quasipolynomial time; our result hence implies that model checking for these $$\mu $$ μ -calculi is in $$\textsc {QP}$$ QP . Moreover, we improve the exponent in known exponential bounds on satisfiability checking.
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Walrand, Jean. "Multiplexing: A." In Probability in Electrical Engineering and Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49995-2_3.

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AbstractThis chapter explores the fluctuations of random variables away from their mean value. You flip a fair coin 100 times. How likely is it that you get 60 heads? Conversely, if you get 60 heads, how likely is it that the coin is fair? Such questions are fundamental in extracting information from data.In Sect. 3.1, we start by exploring the rate available to a user when a random number of them share a link, as illustrated in Sect. 3.1. Such calculations are central to network provisioning. The main analytical tool is the Central Limit Theorem explained in Sect. 3.2 where Gaussian random variables are also introduced and confidence intervals are defined. To share a common link, devices may be attached to a switch. For instance, the desktop computers in a building are typically connected to a switch that then sends the data to a common high-speed link. We explore the delays that packets face through the buffer of a switch in Sect. 3.3. The analysis uses a Markov chain model of the buffer. To share a wireless radio channel, devices use a multiple access protocol that regulates the transmissions. We study such schemes in Sect. 3.4. We use probabilistic models of the protocols.
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Shin, Yong-Jun, Eunho Cho, and Doo-Hwan Bae. "PASTA: An Efficient Proactive Adaptation Approach Based on Statistical Model Checking for Self-Adaptive Systems." In Fundamental Approaches to Software Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71500-7_15.

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AbstractProactive adaptation, in which the adaptation for a system’s reliable goal achievement is performed by predicting changes in the environment, is considered as an effective alternative to reactive adaptation, in which adaptation is performed after observing changes. When predicting the environmental changes, the prediction may be uncertain, so it is necessary to verify and confirm an adaptation’s consequences before execution. To resolve the uncertainty, probabilistic model checking (PMC) has been utilized for verification of adaptation tactics’ effects on the goal of a self-adaptive system (SAS). However, PMC-based approaches have limitations on the state-explosion problem of complex SAS model verification and the modeling languages supported by the model checkers. In this paper, to overcome the limitations of the PMC-based approaches, we propose an efficient Proactive Adaptation approach based on STAtistical model checking (PASTA). Our approach allows SASs to mitigate the uncertainty of the future environment, faster than the PMC-based approach, by producing statistically sufficient samples for verification of adaptation tactics based on statistical model checking (SMC) algorithms. We provide algorithmic processes, a reference architecture, and an open-source implementation skeleton of PASTA for engineers to apply it for SAS development. We evaluate PASTA on two SASs using actual data and show that PASTA is efficient comparing to the PMC-based approach. We also provide a comparative analysis of the advantages and disadvantages of PMC- and SMC-based proactive adaptation to guide engineers’ decision-making for SAS development.
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Kaporis, Alexis C., and Lefteris M. Kirousis. "Proving Conditional Randomness using the Principle of Deferred Decisions." In Computational Complexity and Statistical Physics. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195177374.003.0016.

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In order to prove that a certain property holds asymptotically for a restricted class of objects such as formulas or graphs, one may apply a heuristic on a random element of the class, and then prove by probabilistic analysis that the heuristic succeeds with high probability. This method has been used to establish lower bounds on thresholds for desirable properties such as satisfiability and colorability: lower bounds for the 3-SAT threshold were discussed briefly in the previous chapter. The probabilistic analysis depends on analyzing the mean trajectory of the heuristic—as we have seen in chapter 3—and in parallel, showing that in the asymptotic limit the trajectory’s properties are strongly concentrated about their mean. However, the mean trajectory analysis requires that certain random characteristics of the heuristic’s starting sample are retained throughout the trajectory. We propose a methodology in this chapter to determine the conditional that should be imposed on a random object, such as a conjunctive normal form (CNF) formula or a graph, so that conditional randomness is retained when we run a given algorithm. The methodology is based on the principle of deferred decisions. The essential idea is to consider information about the object as being stored in “small pieces,” in separate registers. The contents of the registers pertaining to the conditional are exposed, while the rest remain unexposed. Having separate registers for different types of information prevents exposing information unnecessarily. We use this methodology to prove various randomness invariance results, one of which answers a question posed by Molloy [402].
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Conference papers on the topic "Probabilistic Analysis of Max 3-Satisfiability"

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Valladares, Homero, and Andres Tovar. "A Simple and Effective Methodology to Perform Multi-Objective Bayesian Optimization: An Application in the Design of Sandwich Composite Armors for Blast Mitigation." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22564.

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Abstract Bayesian optimization is a versatile numerical method to solve global optimization problems of high complexity at a reduced computational cost. The efficiency of Bayesian optimization relies on two key elements: a surrogate model and an acquisition function. The surrogate model is generated on a Gaussian process statistical framework and provides probabilistic information of the prediction. The acquisition function, which guides the optimization, uses the surrogate probabilistic information to balance the exploration and the exploitation of the design space. In the case of multi-objec
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Thakur, Nikita, Andy Keane, and Prasanth B. Nair. "Probabilistic Analysis of Manufacturing Uncertainty in Turbine Blades." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86190.

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An important part in the efficient and robust design of turbine blades is to capture the details of any manufacturing uncertainty. However, the data available detailing the manufacturing uncertainty inevitably contains variability due to inherent errors in any measurement process. The presented work proposes a methodology that employs existing probabilistic data analysis techniques, namely, Principal Component Analysis (PCA), Multivariate Analysis of Variance (MANOVA) and Fast Fourier Transform (FFT) analysis for separation of the measurement error from measurement data to obtain the underlyin
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Arshid, Asif, Ying Huang, and Denver Tolliver. "Probabilistic Structural Analysis of Railroad Subgrade Using Finite Element Analysis." In 2019 Joint Rail Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/jrc2019-1255.

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The reliability of finite element (FE) based deterministic structural analysis of railroad trackbed has improved significantly due to ever increasing computational powers. However, the application of probabilistic methods to incorporate the material and geometric variabilities in these FE analyses is still profoundly underworked. In this study, the influence of variability in granular layers’ modulus and thicknesses values on the railroad subgrade performance have been investigated by applying probabilistic method and using a 3-dimensional FE based numerical trackbed model previously developed
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Li, Kewei, and Wei Sun. "Probabilistic Computational Analysis of Transcatheter Aortic Valve Leaflet Design." In ASME 2013 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/sbc2013-14418.

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Aortic stenosis (AS) is abnormal narrowing of the aortic valve which partially obstructs outflow of blood from the left ventricle to aorta. Symptomatic AS is associated with a high mortality rate, approximately 50% in the first 2 years, if left untreated [1, 2]. Transcatheter aortic valve (TAV) implantation has been recently developed as an effective endovascular treatment for high-risk AS patients, in which a stented bioprosthetic valve is deployed through a catheter within the diseased aortic valve. Since the first procedure in 2002 [3], there has been an explosive growth in TAV implantation
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Nishijima, Kazuyoshi, Michael Havbro Faber, and Marc A. Maes. "Probabilistic Assessment of Extreme Events Subject to Epistemic Uncertainties." In ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57172.

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Over the years the modeling and treatment of aleatory and epistemic uncertainties in probabilistic assessments has repeatedly been an issue of discussion and also some controversy. The philosophical and mathematical aspects may be said to be well appreciated, however, there are cases in practice where principles seem to be violated and frequently the effects of the epistemic uncertainty are treated inconsistently in the probabilistic modeling. The present paper first reviews the general principles for the modeling and treatment of uncertain characteristics subject to both aleatory and epistemi
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Nispel, Abraham, Stephen Ekwaro-Osire, and João Paolo Dias. "Probabilistic Analysis of the Fatigue Life of Offshore Wind Turbine Structures." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14742.

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Abstract The structural response of the main components of offshore wind turbines (OWTs) is considerably sensitive to amplification as their excitation frequencies approach the natural frequency of the structure. Furthermore, uncertainties present in the loading conditions, soil and structural properties highly influence the dynamic response of the OWT. In most cases, the cost of the structure reaches around 30% of the entire OWT because conservative design approaches are employed to ensure its reliability. As a result, this study aims to address the following research question: can the struct
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Hendricks, Terry J., and Naveen K. Karri. "Probabilistic Design and Analysis for Robust Design of Advanced Thermoelectric Conversion Systems." In ASME 2007 Energy Sustainability Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/es2007-36085.

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Recent research investigated the impacts of single- and multi-variable stochasticity on optimum thermoelectric (TE) system design for automotive and industrial energy recovery because many critical design and environmental parameters used in design optimization can be randomly variable. Analysis tools and techniques have been developed to investigate a variety of stochastic behaviors in critical input parameters, including Gaussian, Log-Normal, Weibull, Gamma, or any type of user-defined probability distribution. Recent accomplishments discussed herein show that: 1) Gaussian input probability
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Kay, S., and E. Palix. "Caisson Capacity in Clay: VHM Resistance Envelope: Part 3—Extension to Shallow Foundations." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49077.

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Suction embedded caissons are efficient and economic solutions to anchor floating structures. A more recent caisson application is to support seafloor structures such as manifolds, PLEMs, pumps, etc. For a deepwater hydrocarbon field, many types of seafloor structures are required, each with their own characteristics and slightly different design. Caisson designs increasingly use resistance envelope methodology. This eliminates non-linear 3D FE analyses (except for assessing responses or soil reactions), and facilitates probabilistic and optimisation analyses. In general, there is a requiremen
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Smith, Curtis. "Advanced Small Modular Reactor Probabilistic Risk Assessment Framework." In ASME 2014 Small Modular Reactors Symposium. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/smr2014-3332.

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A key area of the Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) use is in the development of methodologies and tools that will be used to predict the safety, security, safeguards, performance, and deployment viability of SMR systems starting in the design process through the operation phase. Recently, the Idaho National Laboratory (INL) set out to develop quantitative methods and tools and the associated analysis framework for assessing a variety of SMR risks. Development and implementation of SMR-focused safety assessment methods may require new analytic methods or adaptatio
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Xia, Xiaoyang, Eric Nelson, Dan Olds, Larry Connor, and He Zhang. "An Integrated Workflow for Reserves Evaluation in the U.S. Permian Basin Based on SPEE Monograph 3." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206362-ms.

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Abstract In 2011, the Society of Petroleum Evaluation Engineers (SPEE) published Monograph 3 as an industry guideline for reserves evaluation of unconventionals, especially for probabilistic approaches. This paper illustrates the workflow recommended by Monograph 3. The authors also point out some dilemmas one may encounter when applying the guidelines. Finally, the authors suggest remedies to mitigate limitations and improve the utility of the approach. This case study includes about 300 producing shale wells in the Permian Basin. Referring to Monograph 3, analogous wells were identified base
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