{"id":74,"date":"2020-11-14T23:31:04","date_gmt":"2020-11-14T23:31:04","guid":{"rendered":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/?post_type=chapter&p=74"},"modified":"2020-12-12T23:27:50","modified_gmt":"2020-12-12T23:27:50","slug":"calculating-properties","status":"publish","type":"chapter","link":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/chapter\/calculating-properties\/","title":{"raw":"5 Calculating Properties","rendered":"5 Calculating Properties"},"content":{"raw":"Once the three-dimensional framework is defined and gridded, the next step is to populate the grid with the hydraulic property values needed for the simulation\r\n

5.1 Gross Thickness<\/h1>\r\nThe simplest property is the thickness of the reservoir or aquifer, which is the vertical distance between the bounding surfaces (Figure 8).\r\n\r\n[caption id=\"attachment_76\" align=\"alignnone\" width=\"888\"]\"The Figure 8 - <\/strong>The gridded aquifer thickness is the difference between the grid for HO1 and the bedrock surface (Brandenburg, 2020).[\/caption]\r\n\r\n<\/a>For dipping or folded strata, it may be necessary to apply a trigonometric dip correction (Figure 9). In the Test Site example, structural deformation is relatively minor, so no dip correction is applied.\r\n\r\n[caption id=\"attachment_158\" align=\"alignnone\" width=\"614\"]\"Calculating Figure 9 - <\/strong>Calculating true stratigraphic thickness from borehole measurement of a dipping stratum (Brandenburg, 2020).[\/caption]\r\n

5.2 Net Thickness<\/h1>\r\nClays and related rocks such as shale have volumetrically large but disconnected porosity and represent a volume of the aquifer that is not involved in active flow, which is important in evaluating groundwater contamination sites (Payne et al., 2008). The \u201cflowing\u201d portion of the reservoir or aquifer is identified as \u201cnet section\u201d. To determine this, a cutoff-value of coarseness is selected, and the geologic logs are \u201cblocked\u201d into net (coarse sediments) and non-net (fine sediments) zones. The thickness of the net section is tallied for each log, and then used to create contours of net thickness as shown in Figure 10. In this case, the map is an isopach where isocontours represent lines of constant thickness rather than constant elevation.\r\n\r\n[caption id=\"attachment_160\" align=\"alignnone\" width=\"744\"]\"Quantifying Figure 10 - <\/strong>Quantifying Net Thickness with blocked logs and an isopach map (Brandenburg, 2020).[\/caption]\r\n\r\nThe thickness contours are then gridded using the same process as the horizons in the previous steps (Figure 11).\r\n\r\n[caption id=\"attachment_79\" align=\"alignnone\" width=\"889\"]\"Gridded Figure 11 - <\/strong>Gridded Net Thickness Map (Brandenburg, 2020, gw-project.org)[\/caption]\r\n

5.3 Net to Gross Ratio<\/h1>\r\nOnce data for net and gross thickness have been mapped and gridded, it is straightforward to take the ratio of the two. Net thickness divided by total thickness is the net to gross thickness ratio, a value between 0 and 1 that is usually just referred to as net to gross (Figure 12). Regardless of sedimentary facies, high net to gross layers tend to prove both permeable and hydraulically well connected. While low net to gross layers can be permeable at individual wells, they are much more likely to be broken into disconnected compartments.\r\n\r\n[caption id=\"attachment_80\" align=\"alignnone\" width=\"1024\"]\"Gridded Figure 12 - <\/strong>Gridded Net to Gross Ratio (Brandenburg, 2020).[\/caption]\r\n

5.4 Properties<\/h1>\r\nIn oil and gas reservoir appraisal, the net to gross thickness ratio in clastic reservoirs has been long known to correlate reliably with several bulk reservoir properties. Porosity and permeability are often mapped directly from the net to gross value using interpolation functions unique to a particular oil field. At the Test Site, porosity and hydraulic conductivity are estimated based on the correlation between net to gross ratio and the porosity and hydraulic conductivity measurements made in the permanent monitoring wells as shown in Figure 13.\r\n\r\n[caption id=\"attachment_81\" align=\"alignnone\" width=\"1024\"]\" Figure 13 - <\/strong>Relationship between net to gross ratio of each well and the porosity and hydraulic conductivity measurements made in the well (Brandenburg, 2020).[\/caption]\r\n\r\nThe equations for the lines in Figure 13 are (Equations 1 and 2):\r\n\r\n\r\n\r\n
<\/td>\r\n[latex]\\displaystyle Porosity = \\phi =0.3\\left [ \\frac{N}{G} \\right ]+0.035[\/latex]<\/td>\r\n(1)<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n\r\n\r\n\r\n
<\/td>\r\n[latex]\\displaystyle Hydraulic Conductivity=K=(35\\times 10^{-6})\\left [ \\frac{N}{G} \\right ]+(1.8\\times 10^{-6})[\/latex]<\/td>\r\n(2)<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nOnce these aquifer specific relationships have been established, they can be calculated for each grid location given its net to gross value to create aquifer properties for each cell of the 20x20 grid (Figure 14).\r\n\r\n[caption id=\"attachment_84\" align=\"alignnone\" width=\"1024\"]\"Gridded Figure 14 - <\/strong>Gridded porosity and hydraulic conductivity calculated from Equations 1 and 2 (Brandenburg, 2020)[\/caption]","rendered":"

Once the three-dimensional framework is defined and gridded, the next step is to populate the grid with the hydraulic property values needed for the simulation<\/p>\n

5.1 Gross Thickness<\/h1>\n

The simplest property is the thickness of the reservoir or aquifer, which is the vertical distance between the bounding surfaces (Figure 8).<\/p>\n

\"The
Figure 8 – <\/strong>The gridded aquifer thickness is the difference between the grid for HO1 and the bedrock surface (Brandenburg, 2020).<\/figcaption><\/figure>\n

<\/a>For dipping or folded strata, it may be necessary to apply a trigonometric dip correction (Figure 9). In the Test Site example, structural deformation is relatively minor, so no dip correction is applied.<\/p>\n

\"Calculating
Figure 9 – <\/strong>Calculating true stratigraphic thickness from borehole measurement of a dipping stratum (Brandenburg, 2020).<\/figcaption><\/figure>\n

5.2 Net Thickness<\/h1>\n

Clays and related rocks such as shale have volumetrically large but disconnected porosity and represent a volume of the aquifer that is not involved in active flow, which is important in evaluating groundwater contamination sites (Payne et al., 2008). The \u201cflowing\u201d portion of the reservoir or aquifer is identified as \u201cnet section\u201d. To determine this, a cutoff-value of coarseness is selected, and the geologic logs are \u201cblocked\u201d into net (coarse sediments) and non-net (fine sediments) zones. The thickness of the net section is tallied for each log, and then used to create contours of net thickness as shown in Figure 10. In this case, the map is an isopach where isocontours represent lines of constant thickness rather than constant elevation.<\/p>\n

\"Quantifying
Figure 10 – <\/strong>Quantifying Net Thickness with blocked logs and an isopach map (Brandenburg, 2020).<\/figcaption><\/figure>\n

The thickness contours are then gridded using the same process as the horizons in the previous steps (Figure 11).<\/p>\n

\"Gridded
Figure 11 – <\/strong>Gridded Net Thickness Map (Brandenburg, 2020, gw-project.org)<\/figcaption><\/figure>\n

5.3 Net to Gross Ratio<\/h1>\n

Once data for net and gross thickness have been mapped and gridded, it is straightforward to take the ratio of the two. Net thickness divided by total thickness is the net to gross thickness ratio, a value between 0 and 1 that is usually just referred to as net to gross (Figure 12). Regardless of sedimentary facies, high net to gross layers tend to prove both permeable and hydraulically well connected. While low net to gross layers can be permeable at individual wells, they are much more likely to be broken into disconnected compartments.<\/p>\n

\"Gridded
Figure 12 – <\/strong>Gridded Net to Gross Ratio (Brandenburg, 2020).<\/figcaption><\/figure>\n

5.4 Properties<\/h1>\n

In oil and gas reservoir appraisal, the net to gross thickness ratio in clastic reservoirs has been long known to correlate reliably with several bulk reservoir properties. Porosity and permeability are often mapped directly from the net to gross value using interpolation functions unique to a particular oil field. At the Test Site, porosity and hydraulic conductivity are estimated based on the correlation between net to gross ratio and the porosity and hydraulic conductivity measurements made in the permanent monitoring wells as shown in Figure 13.<\/p>\n

\"Relationship
Figure 13 – <\/strong>Relationship between net to gross ratio of each well and the porosity and hydraulic conductivity measurements made in the well (Brandenburg, 2020).<\/figcaption><\/figure>\n

The equations for the lines in Figure 13 are (Equations 1 and 2):<\/p>\n\n\n\n
<\/td>\n\"\displaystyle Porosity = \phi =0.3\left [ \frac{N}{G} \right ]+0.035\"<\/td>\n(1)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
<\/td>\n\"\displaystyle Hydraulic Conductivity=K=(35\times 10^{-6})\left [ \frac{N}{G} \right ]+(1.8\times 10^{-6})\"<\/td>\n(2)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Once these aquifer specific relationships have been established, they can be calculated for each grid location given its net to gross value to create aquifer properties for each cell of the 20×20 grid (Figure 14).<\/p>\n

\"Gridded
Figure 14 – <\/strong>Gridded porosity and hydraulic conductivity calculated from Equations 1 and 2 (Brandenburg, 2020)<\/figcaption><\/figure>\n","protected":false},"author":1,"menu_order":5,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-74","chapter","type-chapter","status-publish","hentry"],"part":3,"_links":{"self":[{"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/pressbooks\/v2\/chapters\/74","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":9,"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/pressbooks\/v2\/chapters\/74\/revisions"}],"predecessor-version":[{"id":173,"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/pressbooks\/v2\/chapters\/74\/revisions\/173"}],"part":[{"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/pressbooks\/v2\/parts\/3"}],"metadata":[{"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/pressbooks\/v2\/chapters\/74\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/wp\/v2\/media?parent=74"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/pressbooks\/v2\/chapter-type?post=74"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/wp\/v2\/contributor?post=74"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/books.gw-project.org\/geologic-frameworks-for-groundwater-flow-models\/wp-json\/wp\/v2\/license?post=74"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}