Geology Reports⌕ Search

Geology topics

C. Özgen Karacan

Publications and source records attributed to C. Özgen Karacan.

At least 37 records · Page 2Linked to original sources
USGS2021

Realizations

In statistics, a realization is an observed value of a random variable (Gubner 2006 ). In mathematical geology, the most important realizations are those in the form of maps of spatially correlated regionalized variables. Spatial description of random variables within complex domains and making certain decisions about those require complete knowledge of the attribute of interest at each point in space. However, it is virtually impossible to sample from every location within the domain to gain a complete spatial understanding of the random variables with certainty at different scales. Therefore, limited sampling leaves us with incomplete information, which is the source of uncertainty. Understanding the uncertainty and quantifying it are essential to minimize the risks of decision making. Geostatistical simulation techniques aim to quantify spatial uncertainty of random variables by numerically reproducing the reality, which we have limited knowledge of, in a discretized...

Book chapter↗
USGS2021

Multilayer perceptrons (MLPs)

Artificial neural networks (ANNs) are adaptable systems that can solve problems that are difficult to describe with a mathematical relationship. They seek relationships between different types of datasets with their abilities to learn either with supervision or without. ANNs recognize patterns between input and output space and generalize solutions, in a way simulating the human brain’s learning experience with many relatively simple individual processing elements, called neurons. Neurons are networked (network topology) in a number of ways depending on the problem type and complexity. One of the most widely used ANN learning techniques is supervised learning coupled with a multilayer perceptron (MLP) topology due to its flexible applicability to a wide range of modeling problems involving both general classification and regression. ANNs, due to this flexibility, have been applied to many fields since the 1990s and their theory, types (such as radial basis functions, random...

Book chapter↗
USGS2021

Assessing gas leakage potential into coal mines from shale gas well failures: Inference from field determination of strata permeability responses to longwall-induced deformations

This paper summarizes the changes in permeability at three boreholes located above an abutment pillar at a longwall coal mine in southwestern Pennsylvania. The motivation of this study was to better characterize the potential interaction between shale gas wells and the mine environment, through measurement of permeability changes in the coal mine overburden caused by mining-induced deformations. Measuring permeability changes around boreholes affected by longwall mining is an effective method to indicate changes in the fracture network above longwall abutment pillars and estimate the capacity for gas flow from shale gas wells to the mine environment. This study measured permeability through falling-head slug tests at different longwall face positions during the mining of two longwall panels on either side of the test abutment pillar where the test boreholes were located. Three test boreholes were drilled to different depths above the active mining level, and they had screened intervals to evaluate the response of different stratigraphic zones to mining-induced stresses. The results showed that the permeability around the slotted intervals of each borehole increased pre-mining to post-mining, and the permeability increased from mining of the first longwall panel to mining of the second one, adjacent to the pillar.

Natural Resources Research↗
USGS2021

Single-well production history matching and geostatistical modeling as proxy to multi-well reservoir simulation for evaluating dynamic reservoir properties of coal seams

Reservoir properties of coal seams such as gas and water effective permeabilities and gas content, as well as spatial distributions thereof, affect the success of gas production and CO 2 -enhanced gas recovery (EGR) with simultaneous CO 2 sequestration. These properties change during production and injection operations due to variations in reservoir pressure, matrix shrinkage/swelling, and water saturation and are therefore referred to as dynamic properties. Predicting distribution of such important reservoir properties and how they evolve during production, or injection, at unsampled locations can be particularly important for field development and project economics. In this work, dynamic properties of Black Creek coal seam of Black Warrior Basin, Alabama were mapped using pointwise results from single-well production history matching of 45 wells and classical geostatistics . It is explored if this approach can be a proxy, with its limitations, to multi-well reservoir simulation. For this purpose, a reservoir model was built using available reservoir, well and production data to compare its results with those of the geostatistical maps for the same properties. Despite the expected local discrepancies due to differences between the two approaches, the results showed similar patterns and global distributions. Specific results showed that despite long-time operation of the wells in this area, there were still areas with high gas content and low gas effective permeability within the modeled time interval that might have benefited from further development using additional wells.

Alabama↗
USGS2021

Insights on the characteristics and sources of gas from an underground coal mine using compositional data analysis

Coal mine gas originates from the gas emission zone (GEZ) of the mine, as well as the longwall face and pillars. Gas emissions are controlled directly at the sources using horizontal or vertical boreholes drilled from surface or from the entries in advance of mining, or it is captured from the fractured and caved zones (gob) using ventholes during mining. The rest of the gas, especially that gas that originates from the longwall face and caved zone, mixes with the ventilation air and travels through bleeder and return entries before being exhausted to atmosphere from air shafts. Although the gas associated with mining mostly focuses on methane, the gas is not pure methane but is a mixture (where both the components and their quantities vary depending on the sampling location). Understanding the evolution of the composition of the gas from source to different sampling and evaluation points in mines using proper statistical analysis and interpretation methods can lead to better designed degasification and ventilation systems and the selection of the most adequate utilization method for generating energy from the gas. In this work, we present the results of compositional data analysis (CoDa) of gases sampled at different locations in a longwall mine operating in Pennsylvanian coal-bearing strata in the Northern Appalachian coal basin. Sampling locations were from accessible parts of the bleeder entries, returns, bleeder evaluation points and shafts within the mine, and also from gob gas ventholes (GGV) and coal degasification boreholes drilled in the panel areas. In addition, desorbed gas samples from seams that are important for the mine were included in the analyses for comparison. The compositional data analysis showed that the gas composition shifts based on the sampling location, putting in-mine and pure coalbed gases on the opposite ends. Removal of air from the samples did not change this observation suggesting that oxygen is depleted especially for in-mine samples, which is due to oxidation. Results also suggested that desorbed gas samples may not represent the composition of the coal seam gas.

International Journal of Coal Geology↗
USGS2020

A two-stage step-wise framework for fast optimization of well placement in coalbed methane reservoirs

Coalbed methane (CBM) has emerged as a clean energy resource in the global energy mix, especially in countries such as Australia, China, India and the USA. The economical and successful development of CBM requires a thorough evaluation and optimization of well placement prior to field-scale exploitation. This paper presents a two-stage, step-wise optimization framework to obtain the optimal placement of wells for large-scale development of CBM reservoirs. In the first stage, an optimal uniform well pattern is obtained by optimizing well pattern description parameters with the particle swarm optimization (PSO) algorithm. Subsequently, the location and status (active/inactive) of each well are perturbed and optimized within the patterns through the integration of the generalized pattern search (GPS) algorithm and a quality map (QM) representing the production potential. This framework was tested in a synthetic anthracite CBM reservoir in the Qinshui basin (with high gas content and low permeability) and a real field high volatile bituminous reservoir in the Illinois basin (with low gas content and high permeability). The results show that: (i) significant variations in the net present value (NPV) exist with respect to different uniform well patterns (even for cases where the total number of wells are identical), the optima of which can be efficiently determined by the PSO within 100 numerical simulation runs; (ii) the optimization of well perturbations by the GPS results in a more noticeable improvement in NPVs for the synthetic (12.3%) than for the real field model (4.6%); (iii) for the low permeable synthetic model with narrow optimal well spacings (320 m × 200 m), the contribution of the optimization of well perturbation to the NPV increment is heavily dependent on the uniform well placement solution; (iv) for the high permeable real field model with large optimal well spacings (1300 m × 1300 m), the initial uniform well placement has a very minor effect on the subsequent well perturbation solutions in terms of NPV; (v) the proposed framework significantly outperforms the conventional well-by-well concatenation procedure in terms of computational efficiency, robustness and optimal criteria set for production potential.

International Journal of Coal Geology↗
USGS2019

A probabilistic assessment methodology for carbon dioxide enhanced oil recovery and associated carbon dioxide retention

The U.S. Energy Independence and Security Act of 2007 authorized the U.S. Geological Survey (USGS) to conduct a national assessment of the potential volume of hydrocarbons recoverable by injection of carbon dioxide (CO 2 ) into known oil reservoirs with historical production. The implementation of CO 2 enhanced oil recovery (CO 2 -EOR) techniques could increase the U.S. recoverable hydrocarbon resource base. Use of anthropogenic CO 2 in the CO 2 -EOR process could reduce the amount of CO 2 released to the atmosphere by allowing a percentage of the injected CO 2 to remain in reservoir pore space once occupied by produced oil and water or by CO 2 dissolution in oil and water in the reservoir. The USGS has developed a new methodology for the national assessment of technically recoverable oil resources that may be produced by using current CO 2 -EOR technologies. The methodology relies on a proprietary reservoir-level database, the comprehensive resource database (CRD). The CRD incorporates commercially available geologic and engineering data, and USGS-defined play averages or province averages of reservoir data were used to populate incomplete records. Values from the CRD are used to estimate the original oil in place ( OOIP ) for each reservoir. The inputs are reviewed by USGS geologists, particularly when play or province averages have been used. Monte Carlo simulation is used to produce a numerical probability distribution for the OOIP for each reservoir, with the mean defined as the value of the OOIP in the CRD. A reservoir model (CO 2 Prophet, developed for the U.S. Department of Energy by Texaco, Inc.) is used to determine the incremental recovery factors for oil during the CO 2 -EOR process, on an individual reservoir basis. The model is also used to estimate the volume of CO 2 remaining in the reservoir after the CO 2 -EOR process is complete. Empirical decline curve analysis and comparison with data from published papers and reports on CO 2 -EOR projects are utilized to substantiate the simulation results. Numerical distributions of recovery factors are prepared for variations in the reservoir lithology (clastic or carbonate). The distribution of incremental oil is computed by multiplying the appropriate probability distribution of recovery factors by the individual reservoir distribution of the OOIP . A way to estimate the CO 2 remaining in the reservoir after the completion of the CO 2 -EOR process is also included in the methodology. Assessment results will be aggregated to play, petroleum province, regional, and national scales. This assessment methodology has been tested on the Horseshoe Atoll, Upper Pennsylvanian-Wolfcampian play in the Permian Basin Province in Texas; the play consists of 27 reservoirs having at least 2 billion barrels of OOIP that are amenable to the CO 2 -EOR process. The play was selected as a test case because CO 2 -EOR production data and published reports are available for several reservoirs within the play. Preliminary estimates of oil recoverable by implementation of miscible CO 2 -EOR are comparable to those reported in the literature and obtained by reservoir decline curve analysis.

Scientific Investigations Report↗
USGS2019

Assessment of coal mine methane (CMM) and abandoned mine methane (AMM) resource potential of longwall mine panels: example from Northern Appalachian Basin, USA

"Coal mine methane (CMM) and abandoned mine methane (AMM), are by-products of underground coal mining. The quantity and the emission rate of CMM and AMM may vary depending on the type of mine, gas content of the mined coal seam, and gas sourced from strata and coal beds in overlying and underlying formations affected by mining. Therefore, if a mine has the potential of accumulating gas after being abandoned and sealed properly, methane may be produced and used as an energy source to serve to local communities around the mine. Producing AMM also prevents methane, which is a potent greenhouse gas, from leaking to the atmosphere through seals, shaft plugs or surface cracks. One of the technical barriers in front of investments to economical utilization of CMM and AMM is the difficulty to predict how much methane may be available in the gas emission zone (GEZ) as a resource during mining, and after the panels are sealed and the mine is abandoned. Another difficulty is to estimate how much of the potential methane resource can be produced, and its production feasibility with boreholes, such as gob gas ventholes (GGV) converted to capture AMM. In this study, a comparative assessment is presented to address the issues stated above. The assessment was conducted on two adjacent panels of a longwall mine that operated until 2016 in the Pennsylvania section of the Northern Appalachian Basin. The study is based on two approaches that might be used depending on the availability of data, extensive or minimal. The first approach uses an extensive geological data set, geostatistics, and measured shaft gas emission and GGV production values that were collected while the panel(s) were active to assess the AMM resource. The second approach uses a minimal amount of geologic data and its uncertainty as probabilistic distributions as well as predicted during-mining emissions using a publicly available software. Results showed that both approaches provide relatively comparable estimates of AMM resources and AMM recovery potential using wellbores. The differences in assessed quantities are mostly due to the characteristics of the two methods. In that regard, this paper can be considered as guidance to choose the assessment approach based on data availability. "

Northern Appalachian Basin↗
USGS2019

Petrology and geochemistry of migrated hydrocarbons associated with the Albert Formation oil shale in New Brunswick, Canada

Samples of the Carboniferous oil shale of the Albert Formation in New Brunswick, Canada, were examined using reflected white and fluorescence light microscopy, Rock-Eval pyrolysis, and ICP-MS (for elements). The presence of fractured filled solid bitumen in contact with, and within the Albert Formation oil shale, particularly in fractures at the right angle to the bedding of oil shale indicated that migrated oil had enough force to overcome the tensil strength of oil shale matrix, and penetrating the oil shale. Migrating fluid also caused thermal alteration of the oil shale matrix, as evident by the presence primary bitumen and oil droplets. The evidence of oil migration included the presence of solid bitumen and crystalline carbonates in contact with the immature oil shale. The low permeability oil shale acted as a seal/aquitard and created a diagenetic ‘front’ by reducing/slowing the advance of migrating oil, resulting in the formation of a reaction zone. Oil droplets were found in this reaction zone. Albertite was the only solid bitumen reported in the Albert Mine area previously. However, the present study found that migrated solid bitumen consisted of both soluble solid bitumen types such as gilsonite and glance-pitch, and non-soluble solid bitumen such as wurtzilite and albertite. The high hydrocarbon yield of oil shales in the Albert Mine area was due to the presence of various solid bitumen types associated with the oil shale and possibly slight thermal alteration that the oil shale experienced when it came in contact with migrating oil. The variation of Th/K ratio and TOC (wt%) indicates that most of oil shales from the Albert Mine area and within the vicinity of oil migration have higher content of TOC (17-25 wt%) as compared to the other Albert oil shales ( TOC=<10 %). The oil shales examined in this study were immature to marginally mature and had a wide range of hydrocarbon yield (2-213 L/Tonne). The highest hydrocarbon yield range (66-199 L/Tonne) was associated with oil shale outcrop samples collected in the Albert Mine itself, where the oil shale was heavily impregnated by migrated solid bitumen.

New Brunswick↗
USGS2019

Insights from Rock-Eval analysis on the influence of sample weight on hydrocarbon generation from Lower Permian organic-matter rich rocks, West Bokaro basin, India

Among the different Rock-Eval parameters, the hydrocarbons released under S2 peak of Rock-Eval is of significance as it indicates the residual hydrocarbon content of the rock. Further, through its relationship with TOC content, it helps in calculating hydrogen indices (HI) which helps in understanding the type of organic-matter present in a rock [HI= (S2/TOC)*100]. The present study documents the role of sample weight/amount used for analysis on Rock-Eval S2 parameter for unconventional source-rock characterization. For the purpose of study, a vitrain-band sample type (manually isolated from a coal), a high-TOC shale sample type, and a carbonaceous shale sample type, were analyzed at two different particle sizes (viz. 1mm-500 microns and 212-75 microns) and different sample weights (5-15 mg for organic-matter rich rocks, and 30-60 mg for shale) using a Rock-Eval basic cycle (heating rate at 25 °C/min). Although S2 is reported as mg HC/ g rock, with increase in sample weight, an increase in hydrocarbons released under S2 peak of Rock-Eval was observed for all three sample types for both the particle sizes. The observations were further validated using a Norwegian geochemical standard (JR-1). Further, all the samples were reanalyzed by conducting pyrolysis experiments at a lower heating rate of 5 °C/min. The impact of sample weight on S2 and hydrogen index (HI) was observed to be more pronounced for the JR-1 standard (higher hydrocarbon yield) than the Types III-IV organic-matter bearing rocks. It thus calls for interpreters to be aware of the influence of mass of organic-matter on hydrocarbon generation, and to monitor the maximum S2 values of organic-matter bearing rocks, within the Flame Ionization Detector (FID) detection limits. Further, it is recommended that for Type III organic-matter bearing rocks with TOC content>20 wt %, elemental analysis should be used to derive atomic H/C and O/C ratios for Van Krevelen diagram-based kerogen typing.

West Bokaro basin↗
USGS2019

An ANCOVA model for porosity and its uncertainty for oil reservoirs based on TORIS dataset

Porosity is one of the most important parameters to assess in-place oil or gas in reservoirs, and to evaluate recovery from enhanced production operations. Since it is relatively well-established to determine porosity using different laboratory and field methods, its value is usually determined at many locations across a reservoir as part of the common practice to capture reservoir heterogeneity and the variability in values. This suite of measurements and the distribution of values are most valuable for probabilistic reservoir assessments, and for spatial modeling if the exact data locations are known. Despite the importance of individual measurements to set the range of values for probabilistic studies, it is not always possible to access these data due to confidentiality. In most cases, commercial or publicly available databases that assessments may rely on usually report only mean values of porosity, like any other reservoir data, or they may not report a value at all. This makes both quantifying the mean value and the uncertainty around it difficult for probabilistic assessments. In this study, the TORIS (Tertiary Oil Recovery Information System) dataset of the National Petroleum Council and the U.S. Department of Energy was used to model porosity and the uncertainty around predicted values. TORIS is an integrated dataset of production data, reservoir properties, and project databases of crude oil reservoirs in the United States. The model presented in the paper was based on ANCOVA (Analysis of Co-Variance) of data from 1038 reservoirs from the TORIS dataset for porosity prediction, validation and testing for quantitative and qualitative parameters that may be readily available in most cases, and to estimate uncertainty around the mean values. This model also explored association of porosity values to different parameters, and to different depositional systems and diagenetic overprint conditions. Furthermore, an ANN (Artificial Neural Network) model was created to compare the predicted values of both models. Results showed that the ANN model was able to represent more of the variability, however it lacked the insights that might be gained from the ANCOVA model.

Journal of Petroleum Science and Engineering↗
USGS2019

A fuzzy logic approach for estimating recovery factors of miscible CO2-EOR projects in the United States

"Recovery factor (RF) is one of the most fundamental parameters that define engineering and economical success of any operational phase in oil and gas production. The effectiveness of the operation, e.g. CO2-EOR (enhanced oil recovery with carbon dioxide injection), is usually defined by multiplying the resultant recovery factor by the original oil in place. Moreover, investment decisions for such engineering projects are also performed based on predicted recovery factors. Despite its importance, though, it is not easy to predict recovery factors as they are affected by many factors including the type of the recovery process, reservoir type, fluid properties, reservoir heterogeneity, depth, thickness, to name a few. The usual method of estimating recovery factors is laboratory experiments or numerical modeling, each of which has their own limitations due to data requirements, boundary conditions and scale effects. In this work, a fuzzy inference system approach has been adopted to predict miscible CO2-EOR recovery factors of the major field applications in the United States with the premise that it can be used as a guidance tool for making decisions based on different inputs. The fuzzy system was build using a Mamdani-type fuzzy logic inference engine, and by using reservoir data compiled from different sources as inputs and recovery factors gathered from a literature survey. Due to the limited number of field cases that could be used for this purpose, 24 sets of applications were included in the study. Selected input variables were water saturation after waterflood (Sorw), well spacing, porosity, permeability, depth, net pay thickness, initial pressure, API gravity of oil, hydrocarbon pore volume CO2 injected, and reservoir lithology. The type of membership functions were decided based on the system’s predictive performance. The model showed reasonable predictive capability for the field observations of recovery factor despite the complexity of this parameter. In addition, since the fuzzy solution was multi-dimensional due to multiple inputs, system behavior was used to demonstrate response of miscible CO2-EOR recovery factor to different inputs. "

Journal of Petroleum Science and Engineering↗
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

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

Zone identification and oil saturation prediction in a waterflooded field: Residual oil zone, East Seminole Field, Texas, Permian Basin

Recently, the miscible CO 2 -EOR tertiary process used in the main pay zone (MP) of suitable reservoirs has broadened to include exploitation of the underlying residual oil zone (ROZ) where a significant amount of oil may remain. The objective of this study is to identify the ROZ and to assess the remaining oil in a brownfield ROZ by using core data and conventional well logs with probabilistic and predictive methods. Core and log data from three wells located in the East Seminole Field in Gaines County, Texas, were used to identify the MP and ROZ in the San Andres Limestone, and to predict oil saturations. The core measurements were used to calculate probabilistic in-situ oil saturations within the MP and the ROZ as a function of depth. Well logs, in combination with core data and calculated saturations, on the other hand, were used to develop two expert systems using artificial neural networks (ANN); one to identify the ROZ and MP, and the other to predict oil saturation. These systems were also supported by a classification and regression tree (CART) analysis to delineate the rules that lead to classifications of zones. Results showed that expert systems developed and calibrated by combining core and well log data can identify MP and ROZ with a success score of more than 90%. Saturations within these zones can be predicted with a correlation coefficient of around 0.6 for testing and 0.8 for training data. The analyses showed that neutron porosity and density well log readings are the most influential ones to identify zones in this field and to predict oil saturations in the MP and ROZ. To explain the relationships of input data with the results, a rule-based system was also applied, which revealed the underlying petrophysical differences between MP and ROZ. This new predictive approach using machine learning techniques, could potentially address the challenges that previous studies have come up against in defining the ROZ within the formation and quantifying remaining oil saturations. The method can potentially be applied to additional fields and help reliably identify the ROZ and estimate saturations for future resource evaluations.

Texas↗
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↗
USGS2013

Time-lapse analysis of methane quantity in Mary Lee group of coal seams using filter-based multiple-point geostatistical simulation

Coal seam degasification and its success are important for controlling methane, and thus for the health and safety of coal miners. During the course of degasification, properties of coal seams change. Thus, the changes in coal reservoir conditions and in-place gas content as well as methane emission potential into mines should be evaluated by examining time-dependent changes and the presence of major heterogeneities and geological discontinuities in the field. In this work, time-lapsed reservoir and fluid storage properties of the New Castle coal seam, Mary Lee/Blue Creek seam, and Jagger seam of Black Warrior Basin, Alabama, were determined from gas and water production history matching and production forecasting of vertical degasification wellbores. These properties were combined with isotherm and other important data to compute gas-in-place (GIP) and its change with time at borehole locations. Time-lapsed training images (TIs) of GIP and GIP difference corresponding to each coal and date were generated by using these point-wise data and Voronoi decomposition on the TI grid, which included faults as discontinuities for expansion of Voronoi regions. Filter-based multiple-point geostatistical simulations, which were preferred in this study due to anisotropies and discontinuities in the area, were used to predict time-lapsed GIP distributions within the study area. Performed simulations were used for mapping spatial time-lapsed methane quantities as well as their uncertainties within the study area. The systematic approach presented in this paper is the first time in literature that history matching, TIs of GIPs and filter simulations are used for degasification performance evaluation and for assessing GIP for mining safety. Results from this study showed that using production history matching of coalbed methane wells to determine time-lapsed reservoir data could be used to compute spatial GIP and representative GIP TIs generated through Voronoi decomposition. Furthermore, performing filter simulations using point-wise data and TIs could be used to predict methane quantity in coal seams subjected to degasification. During the course of the study, it was shown that the material balance of gas produced by wellbores and the GIP reductions in coal seams predicted using filter simulations compared very well, showing the success of filter simulations for continuous variables in this case study. Quantitative results from filter simulations of GIP within the studied area briefly showed that GIP was reduced from an initial ∼73 Bcf (median) to ∼46 Bcf (2011), representing a 37 % decrease and varying spatially through degasification. It is forecasted that there will be an additional ∼2 Bcf reduction in methane quantity between 2011 and 2015. This study and presented results showed that the applied methodology and utilized techniques can be used to map GIP and its change within coal seams after degasification, which can further be used for ventilation design for methane control in coal mines.

Alabama↗