Identifying Products in Thick Coal Seams
Coal Customer Challenge
Several western coal producers in Wyoming and Montana have expressed that power company engineers are requesting specific coal quality constraints to help them more efficiently produce power. While quality constraints get built into a supply contract, power companies have increasingly been asking their coal suppliers to vary the coal quality on an ad hoc basis during the term of the contract. Keeping customers happy in the current coal market is critical to maintaining a viable business. Proving to the customer that they have a reliable and responsive vendor can help ensure customer loyalty and potentially provide an opportunity to get a price premium. The challenge is to find and deliver coal that meets customers’ changing demands in a reasonable amount of time.
Coal Producer Challenge
Companies mining coal in the Powder River Basin and southern Montana region are blessed with thick coal deposits ranging anywhere between 25 and 100 feet. Traditionally, coal quality has been derived using a quality model composited over the full seam thickness. With this approach, a single quality is evaluated for the entire seam thickness. With no ability to consider the variability of quality with depth, defining coal products to meet customer requirements is confined to variation in plan only. Furthermore, if the coal is mined in multiple benches, then the composited coal quality model is a poor indicator of the product that is being mined and shipped to the customer.
Poor quality coal product planning increases the likelihood that you may have an unhappy customer on your hands. To identify the coal quality that is getting shipped, companies may take samples from the bench to get an indication of what the customer will receive. A more broad-brush approach is to waste a few feet of suspected poor-quality zones, such as at the roof of floor of the coal seam. Using this approach, it’s possible good quality coal is wasted, and potentially poor-quality coal is added to the value stream. Wasting good quality coal is costly to the mine and it doesn’t take much for the dollars to mount up. For example, in a 150’ X 1500’ pit, wasting 2 feet of coal results in approximately 18,000 short tons of coal lost. Assuming coal sells at $12.50/ton, companies may be losing $225,000 in one pit. Conversely, adding 18,000 tons of poor-quality coal into the value stream could significantly increase costs if the customer decides to reject the shipment.
A Different Approach
By modelling the variation of quality with depth in thick coal seams, coal producers have more opportunities to identify zones that can meet customer requirements that may change at short notice. The general process to achieve a higher fidelity of product identification using MineScape is described below.
Sampling
To model quality variation with depth, assay sampling need to be performed through the thickness of the coal seam. From a geostatistics perspective, it’s desirable that the sample lengths are fixed to a single thickness, but in the quality modelling process described later, it’s not mandatory. The sample length should be determined by the geologist who is familiar with the coal deposit variability and geology, but a sample length of 2 to 5 feet is typically appropriate. More granularity in the sampling will improve product identification, but this must be balanced against practical mining limits including minimum mining thickness and minimum separable parting, as well as cost.
Geologic Modelling
Modelling the coal structure using exploration drillholes and any useful survey data into a grid seam model is the next step in the process. The grid seam model will define roof, floor and thickness structures of the coal seam or seams. The grid seam structure model is critical as this will have a profound impact on the quality interpolation, as we will see later.
Once the grid seam modelling is completed, it can be intersected with a block model that contains all relevant quality parameters. The block model dimensions should be defined with respect to sample spacing and mining constraints. The MineScape block model cells carry information about the interval they belong to in the Stratmodel grid, as well as their position within the interval. Block Model sub-celling is applied to better honour the contact between the interval roof and floor surfaces within the block model.
Quality Modelling
Quality modelling is accomplished by interpolating the coal assay samples into the block model cells. In addition to universal or ordinary Kriging interpolation, MineScape provides inverse distance with interval following, which has proven to provide excellent results in thick seam stratigraphic deposits. A rich selection of inverse distance interpolator parameters is provided to define the search volume, minimum and maximum samples per cell, and octant and inverse power value. With interval following, sample location within the interval is considered during interpolation of sample values into block model cells. Since the interval structure controls the interpolation, extensive geostatistical analyses to define direction is not required, simplifying the process. Each cell attribute can be reported by hovering over any block model cell.
Product Identification
Having the quality variation modelled through the coal thickness opens possibilities to identify different coal products defined by coal quality. The block model can be queried for cells that meet required quality criteria, enabling producers to identify if, and where product specifications can be met. For example, if the product specification requires coal ash to be 6% or less and Btu to be 8600 or greater, then the product is defined by flagging block model cells with these constraints. Depending on minimum separable parting mining constraints, thin bands of coal that don’t meet product specification can be evaluated to determine its impact on product quality. Evaluating possible inclusion of block model cells above and below those cells meeting quality criteria should be performed to maximize coal extraction while at the same time, meet product quality constraints.
Once the products are identified in the model, roof and floor surfaces can be created to assist with planning practical mining benches that when mined will deliver specific coal products. Additionally, reserve calculations can be categorized within polygons and solids by product, bench, quality etc.
Conclusion
The U.S. thermal coal mining industry is in decline due to external forces of cheap natural gas as well as a political and social desire to increase renewable sources of power generation. According to the EIA, it’s predicted that thermal coal will continue to lose U.S. power generation market share in the near term while levelling off to somewhere 13-15% in the mid-2020s.
Coal producers are competing for a smaller market and experiencing stricter coal quality requests from their power generator customers, whether through new contract negotiations or ad hoc requests. Low-cost coal has always been a factor in purchasing decisions, however, power companies also consider supply reliability and coal products that enable them to meet emission regulations and improve boiler efficiencies. It’s possible a low-cost coal product could increase operating costs for the power company.
Coal producers in the Powder River Basin in Wyoming and in Montana are blessed with thick coal seams. Traditional coal quality modelling employs a composited approach that doesn’t identify the variation of quality through the coal thickness; thereby, limiting the ability to identify products. By gathering detailed assay quality data through the coal thickness, quality modelling can get more detailed and enable identification of coal products based on specified quality parameters. With one or more coal products identified in situ, mining engineers can determine if they can meet customer demand and then improve shipment plans providing coal customers with confidence that their supplier can reliably meet their supply requirements. Reacting to ad hoc requests from coal customers can also be improved. With a simple query into the block model, coal producers can determine if they have the product required and if so, where and how they can deliver it. Providing the best products and services to coal customers will help coal producers weather the current declining thermal coal market.
