Geology topics

Ricardo A. Olea

Publications and source records attributed to Ricardo A. Olea.

At least 37 records · Page 2Linked to original sources

USGS2019

Self-organizing maps for compositional data: coal combustion products of a Wyoming power plant

A self-organizing map (SOM) is a non-linear projection of a D-dimensional data set, where the distance among observations is approximately preserved on to a lower dimensional space. The SOM arranges multivariate data based on their similarity to each other by allowing pattern recognition leading to easier interpretation of higher dimensional data. The SOM algorithm allows for selection of different map topologies, distances and parameters, which determine how the data will be organized on the map. In the particular case of compositional data (such as elemental, mineralogical, or maceral abundance), the sample space is governed by Aitchison geometry and extra steps are required prior to their SOM analysis. Following the principle of working on log-ratio coordinates, the simplicial operations and the Aitchison distance, which are appropriate elements for the SOM, are presented. With this structure developed, a SOM using Aitchison geometry is applied to properly interpret elemental data from combustion products (bottom ash, fly ash, and economizer fly ash) in a Wyoming coal-fired power plant. Results from this effort provide knowledge about the differences between the ash composition in the coal combustion process.

Stochastic Environmental Research and Risk Assessm↗

USGS2019

Carbon dioxide enhanced oil recovery and residual oil zone studies at the U.S. Geological Survey

The U.S. Geological Survey (USGS) is preparing a national resource assessment of the potential hydrocarbons recoverable after injection of carbon dioxide (CO2) into conventional oil reservoirs in the United States. The implementation of CO2-enhanced oil recovery (CO2-EOR) techniques can increase hydrocarbon production, and lead to incidental retention of CO2 in reservoir pore space allowing long-term storage of anthropogenic CO2. A Comprehensive Resource Database (CRD) containing proprietary data on location, geologic, petrophysical, and reservoir parameters, plus production and well counts for major oil and gas reservoirs in onshore areas and State waters of the conterminous United States and Alaska, was developed to support the USGS assessment. Residual oil zones (ROZs) also can provide potential pore space for long-term storage of anthropogenic CO2. However, ROZs are not included in the upcoming USGS national CO2-EOR assessment because assessment methods for ROZs still are being developed. Additional ROZ CO2-EOR and CO2 retention data and reservoir simulations are needed to calibrate national ROZ assessment estimates.

Conference Paper↗

USGS2018

Advances in sensitivity analysis of uncertainty to changes in sampling density when modeling spatially correlated attributes

A comparative analysis of distance methods, kriging and stochastic simulation is conducted for evaluating their capabilities for predicting fluctuations in uncertainty due to changes in spatially correlated samples. It is concluded that distance methods lack the most basic capabilities to assess reliability despite their wide acceptance. In contrast, kriging and stochastic simulation offer significant improvements by considering probabilistic formulations that provide a basis on which uncertainty can be estimated in a way consistent with practices widely accepted in risk analysis. Additionally, using real thickness data of a coal bed, it is confirmed once more that stochastic simulation outperforms kriging.

Book chapter↗

USGS2018

Compositional data analysis of coal combustion products with an application to a Wyoming power plant

A mathematically sound approach for summarizing chemical analyses of feed coal and all its combustion products (bottom ash, economizer fly ash, and fly ash) is presented. The nature of the data requires the application of compositional techniques when conducting statistical analysis, techniques that have not been applied before to the study of partitioning of elements between the coal that enters the boiler and the associated coal combustion products. A collection of descriptive and inferential compositional techniques was used to analyze the coal combustion products from a Wyoming power plant burning Paleocene Wyodak–Anderson coal. The significance of the fluctuation in ash composition is determined by using a Hotelling’s T -squared test and bootstrapping. Tree displays allow for visualization of the progressive effect of filters in removal of chemical species based on their geochemical composition. Results indicate that, in general, as the suspended combustion products entrained in the flue gases move closer to the stack, chemical species are removed from the combustion gas, starting with minerals associated with elements having the lowest volatility.

Mathematical Geosciences↗

USGS2018

Gas emissions, tars, and secondary minerals at the Ruth Mullins and Tiptop coal mine fires

Both the Tiptop and Ruth Mullins coal fires, Kentucky, were reinvestigated in 2009 and 2010. The Tiptop fire was not as active in 2009 and may have been on the path to burning out at the time of the 2009 visit. The Ruth Mullins coal mine fire, Perry County, Kentucky, has been the subject of several field investigations, including November 2009–February 2010 investigations in which we measured gas emissions, collected minerals and tars, and characterized the nature of the fire. Vents exhibiting the greatest gas flux (>100,000 mg/s/m 2 ) are those with the largest amount of condensate minerals and tars. Vents with moderate gas flux (10,000–100,000 mg/s/m 2 ) are less likely to contain condensate minerals, but are collocated with tars, and vents with the lowest flux (<10,000 mg/s/m 2 ) generally lack both minerals and tars. Aliphatic hydrocarbons present in the gases include C1-C9 compounds, and aromatics include BTEX compounds. Diffuse-CO 2 emissions are concentrated along the fracture zones overlying abandoned mine works. The area of peak diffuse flux corresponds to the trend of the collapsed portal that forms vent 5. The greatest vent emissions were also recorded at vent 5. The snow-melt zone mapped in January 2010 overlies the areas of peak diffuse-CO 2 emissions measured in November; together they delineate the zone of active combustion. Comparison of greenhouse gas emissions from the two sources shows that vent emissions exceed diffuse emissions. The highly fractured, quartz-cemented roof rock funnels the majority of emissions toward the vents. Significant decreases are seen in estimates of yearly greenhouse emissions based on data gathered from November 2009 to February 2010, with estimates from November significantly exceeding any previously published estimates. For example, September 2009 estimates from vent 3 alone indicated that 19 ± 7.5 T CO 2 /yr were emitted while the November 2009 estimates were 1800 ± 690 T/yr. Barometric pressure was lower in November than September. This implies that there are many factors influencing the seasonal variations in fire emissions and that more frequent monitoring will be necessary to derive accurate estimates of coal fires' contribution to the carbon budget.

Kentucky↗

USGS2018

Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation – A comparative study for spatial ultimate analyses data

Chemical properties of coal largely determine coal handling, processing, beneficiation methods, and design of coal-fired power plants. Furthermore, these properties impact coal strength, coal blending during mining, as well as coal's gas content, which is important for mining safety. In order for these processes and quantitative predictions to be successful, safer, and economically feasible, it is important to determine and map chemical properties of coals accurately in order to infer these properties prior to mining. Ultimate analysis quantifies principal chemical elements in coal. These elements are C, H, N, S, O, and, depending on the basis, ash, and/or moisture. The basis for the data is determined by the condition of the sample at the time of analysis, with an “as-received” basis being the closest to sampling conditions and thus to the in-situ conditions of the coal. The parts determined or calculated as the result of ultimate analyses are compositions, reported in weight percent, and pose the challenges of statistical analyses of compositional data. The treatment of parts using proper compositional methods may be even more important in mapping them, as most mapping methods carry uncertainty due to partial sampling as well. In this work, we map the ultimate analyses parts of the Springfield coal from an Indiana section of the Illinois basin, USA, using sequential Gaussian simulation of isometric log-ratio transformed compositions. We compare the results with those of direct simulations of compositional parts. We also compare the implications of these approaches in calculating other properties using correlations to identify the differences and consequences. Although the study here is for coal, the methods described in the paper are applicable to any situation involving compositional data and its mapping.

Indiana↗

USGS2018

A practical primer on geostatistics

Introduction The Challenge —Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer. The Aim of Geostatistics —The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps. Geostatistical Methods —Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods. Differences with Time Series —On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has significant methodological implications. Historical Remarks —As a discipline, geostatistics was firmly established in the 1960s by the French engineer Georges Matheron, who was interested in the appraisal of ore reserves in mining. Geostatistics did not develop overnight. Like other disciplines, it has built on previous results, many of which were formulated with different objectives in various fields. Pioneers —Seminal ideas conceptually related to what today we call geostatistics or spatial statistics are found in the work of several pioneers, including: 1940s: A.N. Kolmogorov in turbulent flow and N. Wiener in stochastic processing; 1950s: D. Krige in mining; 1960s: B. Mathern in forestry and L.S. Gandin in meteorology Calculations —Serious applications of geostatistics require the use of digital computers. Although for most geostatistical techniques rudimentary implementation from scratch is fairly straightforward, coding programs from scratch is recommended only as part of a practice that may help users to gain a better grasp of the formulations. Software —For professional work, the reader should employ software packages that have been thoroughly tested to handle any sampling scheme, that run as efficiently as possible, and that offer graphic capabilities for the analysis and display of results. This primer employs primarily the package Stanford Geomodeling Software (SGeMS) - recently developed at the Energy Resources Engineering Department at Stanford University - as a way to show how to obtain results practically. This applied side of the primer should not be interpreted as the notes being a manual for the use of SGeMS. The main objective of the primer is to help the reader gain an understanding of the fundamental concepts and tools in geostatistics. Organization of the Primer —The chapters of greatest importance are those covering kriging and simulation. All other materials are peripheral and are included for better comprehension of these main geostatistical modeling tools. The choice of kriging versus simulation is often a big puzzle to the uninitiated, let alone the different variants of both of them. Chapters 14, 18, and 19 are intended to shed light on those subjects. The critical aspect of assessing and modeling spatial correlation is covered in chapter 7. Chapters 2 and 3 review relevant concepts in classical statistics. Course Objectives —This course offers stochastic solutions to common problems in the characterization of complex geological systems. At the end of the course, participants should have: an understanding of the theoretical foundations of geostatistics; a good grasp of its possibilities and limitations; and reasonable familiarity with the SGeMS software, thus opening the possibility of practically applying geostatistics.

Open-File Report↗

USGS2017

Carbon dioxide enhanced oil recovery performance according to the literature

Introduction The need to increase the efficiency of oil recovery and environmental concerns are bringing to prominence the use of carbon dioxide (CO 2 ) as a tertiary recovery agent. Assessment of the impact of flooding with CO 2 all eligible reservoirs in the United States not yet undergoing enhanced oil recovery (EOR) requires making the best possible use of the experience gained in 40 years of applications. Review of the publicly available literature has located relevant CO 2 -EOR information for 53 units (fields, reservoirs, pilot areas) in the United States and 17 abroad. As the world simultaneously faces an increasing concentration of CO 2 in the atmosphere and a higher demand for fossil fuels, the CO 2 -EOR process continues to gain popularity for its efficiency as a tertiary recovery agent and for the potential for having some CO 2 trapped in the subsurface as an unintended consequence of the enhanced production (Advanced Resources International and Melzer Consulting, 2009). More extensive application of CO 2 -EOR worldwide, however, is not making it significantly easier to predict the exact outcome of the CO 2 flooding in new reservoirs. The standard approach to examine and manage risks is to analyze the intended target by conducting laboratory work, running simulation models, and, finally, gaining field experience with a pilot test. This approach, though, is not always possible. For example, assessment of the potential of CO 2 -EOR at the national level in a vast country such as the United States requires making forecasts based on information already available. Although many studies are proprietary, the published literature has provided reviews of CO 2 -EOR projects. Yet, there is always interest in updating reports and analyzing the information under new perspectives. Brock and Bryan (1989) described results obtained during the earlier days of CO 2 -EOR from 1972 to 1987. Most of the recovery predictions, however, were based on intended injections of 30 percent the size of the reservoir’s hydrocarbon pore volume (HCPV), and the predictions in most cases badly missed the actual recoveries because of the embryonic state of tertiary recovery in general and CO 2 flooding in particular at the time. Brock and Bryan (1989), for example, reported for the Weber Sandstone in the Rangely oil field in Colorado, an expected recovery of 7.5 percent of the original oil in place (OOIP) after injecting a volume of CO 2 equivalent to 30 percent of the HCPV, but Clark (2012) reported that after injecting a volume of CO 2 equivalent to 46 percent of the HCPV, the actual recovery was 4.8 percent of the OOIP. Decades later, the numbers by Brock and Bryan (1989) continue to be cited as part of expanded reviews, such as the one by Kuuskraa and Koperna (2006). Other comprehensive reviews including recovery factors are those of Christensen and others (2001) and Lake and Walsh (2008). The Oil and Gas Journal (O&GJ) periodically reports on active CO 2 -EOR operations worldwide, but those releases do not include recovery factors. The monograph by Jarrell and others (2002) remains the most technically comprehensive publication on CO 2 flooding, but it does not cover recovery factors either. This chapter is a review of the literature found in a search for information about CO 2 -EOR. It has been prepared as part of a project by the U.S. Geological Survey (USGS) to assess the incremental oil production that would be technically feasible by CO 2 flooding of all suitable oil reservoirs in the country not yet undergoing tertiary recovery.

Scientific Investigations Report↗

USGS2017

Advancements in hydrochemistry mapping: methods and application to groundwater arsenic and iron concentrations in Varanasi, Uttar Pradesh, India

The area east of Varanasi is one of numerous places along the watershed of the Ganges River with groundwater concentrations of arsenic surpassing the maximum value of 10 parts per billion (ppb) recommended by the World Health Organization in drinking water. Here we apply geostatistics and compositional data analysis for the mapping of arsenic and iron to help in understanding the conditions leading to the occurrence of elevated level of arsenic in groundwater. The methodology allows for displaying concentrations of arsenic and iron as maps consistent with the limited information from 95 water wells across an area of approximately 210 km 2 ; visualization of the uncertainty associated with the sampling; and summary of the findings in the form of probability maps. For thousands of years, Varanasi has been on the erosional side in a meander of the river that is free of arsenic values above 10 ppb. Maps reveal two anomalies of high arsenic concentrations on the depositional side of the valley, which has started seeing urban development. The methodology using geostatistics combined with compositional data analysis is completely general, so this study could be used as a prototype for hydrochemistry mapping in other areas.

Uttar Pradesh↗

USGS2017

A database and probabilistic assessment methodology for carbon dioxide enhanced oil recovery and associated carbon dioxide retention in the United States

The U.S. Geological Survey (USGS) has developed an assessment methodology for estimating the potential incremental technically recoverable oil resources resulting from carbon dioxide-enhanced oil recovery (CO 2 -EOR) in reservoirs with appropriate depth, pressure, and oil composition. The methodology also includes a procedure for estimating the CO 2 that remains in the reservoir after the CO 2 -EOR process is complete. The methodology relies on a reservoir-level database that incorporates commercially available geologic and engineering data. The mathematical calculations of this assessment methodology were tested and produced realistic results for the Permian Basin Horseshoe Atoll, Upper Pennsylvanian-Wolfcampian Play (Texas, USA). The USGS plans to use the new methodology to conduct an assessment of technically recoverable hydrocarbons and associated CO 2 sequestration resulting from CO 2 -EOR in the United States.

Energy Procedia↗

USGS2016

Resampling of spatially correlated data with preferential sampling for the estimation of frequency distributions and semivariograms

Spatial data are commonly minimal and may have been collected in the process of confirming the profitability of a mining venture or investigating a contaminated site. In such situations, it is common to have measurements preferentially taken in the most critical areas (sweet spots, allegedly contaminated areas), thus conditionally biasing the sample. While preferential sampling makes good practical sense, its direct use leads to distorted sample moments and percentiles. Spatial clusters are a problem that has been identified in the past and solved with approaches ranging from ad hoc solutions to highly elaborate mathematical formulations, covering mostly the effect of clustering on the cumulative frequency distribution. The method proposed here is a form of resample, free of special assumptions, does not use weights to ponder the measurements, does not find solutions by successive approximation and provides variability in the results. The new method is illustrated with a synthetic dataset with an exponential semivariogram and purposely generated to follow a lognormal distribution. The lognormal distribution is both difficult to work with and typical of many attributes of practical interest. Testing of the new solution shows that sample subsets derived from resampled datasets can closely approximate the true probability distribution and the semivariogram, clearly outperforming the original preferentially sampled data.

Stochastic Environmental Research and Risk Assessm↗

USGS2016

Calorific value and compositional ultimate analysis with a case study of a Texas lignite

Measurements to determine coal quality as fuel include proximate analysis, ultimate analysis and calorific value. The latter is an attribute taking non-negative real values, so a simple transformation is sufficient for its spatial modeling applying geostatistics. The analyses, however, involve proportions that follow the properties of compositional data, thus requiring special preprocessing for an adequate modeling already described in a previous publication for the case of proximate analysis data. 1 Here we model the results of calorific value and ultimate analysis. We propose to use two different binary partitions, one per analysis, map the corresponding isometric logratio transformations, and backtransform the results. The methodology is illustrated using the same coal bed in the previous paper modeling proximate analysis data. Results are summarized using probability maps that, in the case of this deposit, show a prominent channel crossing the deposit and separating the best quality coal from that of lower quality.

International Journal of Coal Geology↗

USGS2015

Mapping of coal quality using stochastic simulation and isometric logratio transformation with an application to a Texas lignite

Coal is a chemically complex commodity that often contains most of the natural elements in the periodic table. Coal constituents are conventionally grouped into four components (proximate analysis): fixed carbon, ash, inherent moisture, and volatile matter. These four parts, customarily measured as weight losses and expressed as percentages, share all properties and statistical challenges of compositional data. Consequently, adequate modeling should be done in terms of a logratio transformation, a requirement that is commonly overlooked by modelers. The transformation of choice is the isometric logratio transformation because of its geometrical and statistical advantages. The modeling is done through a series of realizations prepared by applying sequential simulation for the purpose of displaying the parts in maps incorporating uncertainty. The approach makes realistic assumptions and the results honor the data and basic considerations, such as percentages between 0 and 100, all four parts adding to 100% at any location in the study area, and a style of spatial fluctuation in the realizations equal to that of the data. The realizations are used to prepare different results, including probability distributions across a deposit, E-type maps displaying average properties, and probability maps summarizing joint fluctuations of several parts. Application of these maps to a lignite bed clearly delineates the deposit boundary, reveals a channel cutting across, and shows that the most favorable coal quality is to the north and deteriorates toward the southeast.

International Journal of Coal Geology↗

USGS2015

Stochastic reservoir simulation for the modeling of uncertainty in coal seam degasification

Coal seam degasification improves coal mine safety by reducing the gas content of coal seams and also by generating added value as an energy source. Coal seam reservoir simulation is one of the most effective ways to help with these two main objectives. As in all modeling and simulation studies, how the reservoir is defined and whether observed productions can be predicted are important considerations. Using geostatistical realizations as spatial maps of different coal reservoir properties is a more realistic approach than assuming uniform properties across the field. In fact, this approach can help with simultaneous history matching of multiple wellbores to enhance the confidence in spatial models of different coal properties that are pertinent to degasification. The problem that still remains is the uncertainty in geostatistical simulations originating from the partial sampling of the seam that does not properly reflect the stochastic nature of coal property realizations. Stochastic simulations and using individual realizations, rather than E-type, make evaluation of uncertainty possible. This work is an advancement over Karacan et al. (2014) in the sense of assessing uncertainty that stems from geostatistical maps. In this work, we batched 100 individual realizations of 10 coal properties that were randomly generated to create 100 bundles and used them in 100 separate coal seam reservoir simulations for simultaneous history matching. We then evaluated the history matching errors for each bundle and defined the single set of realizations that would minimize the error for all wells. We further compared the errors with those of E-type and the average realization of the best matches. Unlike in Karacan et al. (2014), which used E-type maps and average of quantile maps, using these 100 bundles created 100 different history match results from separate simulations, and distributions of results for in-place gas quantity, for example, from which uncertainty in coal property realizations could be evaluated. The study helped to determine the realization bundle that consisted of the spatial maps of coal properties, which resulted in minimum error. In addition, it was shown that both E-type and the average of realizations that gave the best match for invidual approximated the same properties resonably. Moreover, the determined realization bundle showed that the study field initially had 151.5 million m 3 (cubic meter) of gas and 1.04 million m 3 water in the coal, corresponding to Q90 of the entire range of probability for gas and close to Q75 for water. In 2013, in-place fluid amounts decreased to 138.9 million m 3 and 0.997 million m 3 for gas and water, respectively.

Indiana↗

USGS2015

Cokriging of compositional balances including a dimension reduction and retrieval of original units

Compositional data constitutes a special class of quantitative measurements involving parts of a whole. The sample space has an algebraic-geometric structure different from that of real-valued data. A subcomposition is a subset of all possible parts. When compositional data values include geographical locations, they are also regionalized variables. In the Earth sciences, geochemical analyses are a common form of regionalized compositional data. Ordinarily, there are measurements only at data locations. Geostatistics has proven to be the standard for spatial estimation of regionalized variables but, in general, the compositional character of the geochemical data has been ignored. This paper presents in detail an application of cokriging for the modelling of compositional data using a method that is consistent with the compositional character of the data. The uncertainty is evaluated by a Monte Carlo procedure. The method is illustrated for the contents of arsenic and iron in groundwaters in Bangladesh, which have the peculiarity of being measured in milligrams per litre, units for which the sum of all parts does not add to a constant. Practical results include maps of estimates of the geochemical elements in the original concentration units, as well as measures of uncertainty, such as the probability that the concentration may exceed a given threshold. Results indicate that probabilities of exceedance in previous studies of the same data are too low.

Journal of the Southern African Institute of Minin↗

USGS2015

Robust and resistant semivariogram modelling using a generalized bootstrap

The bootstrap is a computer-intensive resampling method for estimating the uncertainty of complex statistical models. We expand on an application of the bootstrap for inferring semivariogram parameters and their uncertainty. The model fitted to the median of the bootstrap distribution of the experimental semivariogram is proposed as an estimator of the semivariogram. The proposed application is not restricted to normal data and the estimator is resistant to outliers. Improvements are more significant for data-sets with less than 100 observations, which are those for which semivariogram model inference is the most difficult. The application is illustrated by using it to characterize a synthetic random field for which the true semivariogram type and parameters are known.

Journal of the Southern African Institute of Minin↗

USGS2014

A bootstrap estimation scheme for chemical compositional data with nondetects

The bootstrap method is commonly used to estimate the distribution of estimators and their associated uncertainty when explicit analytic expressions are not available or are difficult to obtain. It has been widely applied in environmental and geochemical studies, where the data generated often represent parts of whole, typically chemical concentrations. This kind of constrained data is generically called compositional data, and they require specialised statistical methods to properly account for their particular covariance structure. On the other hand, it is not unusual in practice that those data contain labels denoting nondetects, that is, concentrations falling below detection limits. Nondetects impede the implementation of the bootstrap and represent an additional source of uncertainty that must be taken into account. In this work, a bootstrap scheme is devised that handles nondetects by adding an imputation step within the resampling process and conveniently propagates their associated uncertainly. In doing so, it considers the constrained relationships between chemical concentrations originated from their compositional nature. Bootstrap estimates using a range of imputation methods, including new stochastic proposals, are compared across scenarios of increasing difficulty. They are formulated to meet compositional principles following the log-ratio approach, and an adjustment is introduced in the multivariate case to deal with nonclosed samples. Results suggest that nondetect bootstrap based on model-based imputation is generally preferable. A robust approach based on isometric log-ratio transformations appears to be particularly suited in this context. Computer routines in the R statistical programming language are provided.

Journal of Chemometrics↗

USGS2014

Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation

Prediction of potential methane emission pathways from various sources into active mine workings or sealed gobs from longwall overburden is important for controlling methane and for improving mining safety. The aim of this paper is to infer strata separation intervals and thus gas emission pathways from standard well log data. The proposed technique was applied to well logs acquired through the Mary Lee/Blue Creek coal seam of the Upper Pottsville Formation in the Black Warrior Basin, Alabama, using well logs from a series of boreholes aligned along a nearly linear profile. For this purpose, continuous wavelet transform (CWT) of digitized gamma well logs was performed by using Mexican hat and Morlet, as the mother wavelets, to identify potential discontinuities in the signal. Pointwise Hölder exponents (PHE) of gamma logs were also computed using the generalized quadratic variations (GQV) method to identify the location and strength of singularities of well log signals as a complementary analysis. PHEs and wavelet coefficients were analyzed to find the locations of singularities along the logs. Using the well logs in this study, locations of predicted singularities were used as indicators in single normal equation simulation (SNESIM) to generate equi-probable realizations of potential strata separation intervals. Horizontal and vertical variograms of realizations were then analyzed and compared with those of indicator data and training image (TI) data using the Kruskal–Wallis test. A sum of squared differences was employed to select the most probable realization representing the locations of potential strata separations and methane flow paths. Results indicated that singularities located in well log signals reliably correlated with strata transitions or discontinuities within the strata. Geostatistical simulation of these discontinuities provided information about the location and extents of the continuous channels that may form during mining. If there is a gas source within their zone of influence, paths may develop and allow methane movement towards sealed or active gobs under pressure differentials. Knowledge gained from this research will better prepare mine operations for potential methane inflows, thus improving mine safety.

Journal of Applied Geophysics↗