In ScottHMcKean/duvernay_geomech_model: A Geostatistical Model of Geomechanical Properties in the Duvernay Play

library(duvernaygeomechmodel)

This notebook loads static (unconfined compressive strength and triaxial) and dynamic (ultrasonic pulse transmission) data. It merges these two datasets in preparation for statistical model generation and does some exploratory data analysis.

Load files

static_df = read_csv('../data/static_tests.csv')
upt_df = read_csv('../data/upt_tests.csv')

Merge triaxial and ultrasonic data

First we nest the static data, omitting null values (mainly from bulk density). and grouping by well, depth, orientation, confining pressure, and deviatoric stress. We fit a model of Vp and Vs against confining and deviatoric stress for each ultrasonic measurement.

static_nest_no_res <- static_df %>% 
  dplyr::select(-yield_stress_dev, -residual_stress_dev) %>%
  mutate(deviatoric_stress = peak_stress_dev / 2) %>%
  na.omit() %>%
  mutate(conf_pres_group = confining_pressure, 
         dev_stress_group = deviatoric_stress,
         well_group = well,
         depth_group = round_depth,
         orient_group = orientation) %>%
  group_by(well_group, depth_group, orient_group, conf_pres_group, dev_stress_group) %>%
  nest() %>%
  rename(confining_pressure = conf_pres_group, 
         deviatoric_stress = dev_stress_group,
         well = well_group,
         round_depth = depth_group,
         orientation = orient_group)

static_nest_w_res <- static_df %>% 
  dplyr::select(-yield_stress_dev) %>%
  mutate(deviatoric_stress = peak_stress_dev / 2) %>%
  na.omit() %>%
  mutate(conf_pres_group = confining_pressure, 
         dev_stress_group = deviatoric_stress,
         well_group = well,
         depth_group = round_depth,
         orient_group = orientation) %>%
  group_by(well_group, depth_group, orient_group, conf_pres_group, dev_stress_group) %>%
  nest() %>%
  rename(confining_pressure = conf_pres_group, 
         deviatoric_stress = dev_stress_group,
         well = well_group,
         round_depth = depth_group,
         orientation = orient_group)

upt_nest <- upt_df %>%
  group_by(well, round_depth, orientation) %>%
  nest() %>%
  mutate(vp_model = map(data, fit_vp_model)) %>%
  mutate(vs_model = map(data, fit_vs_model))

We then use these models to provide a prediction of Vp and Vs for the conditions present in each triaxial test, for the static dataframes with and without residual stresses. This chunk merges the tests with or without residual stress results.

vp_df <- upt_nest %>%
  inner_join(static_nest_no_res, by = c('well','round_depth','orientation')) %>%
  rename(upt_data = data.x, trx_data = data.y) %>%
  map2_df(.x = .$vp_model, .y = .$trx_data, 
          .f = ~augment(.x, newdata = .y, se_fit=TRUE)) %>%
  rename(pred_vp = .fitted, se_vp = .se.fit)

vs_df <- upt_nest %>%
  inner_join(static_nest_no_res, by = c('well','round_depth','orientation')) %>%
  rename(upt_data = data.x, trx_data = data.y) %>%
  map2_df(.x = .$vs_model, .y = .$trx_data, 
          .f = ~augment(.x, newdata = .y, se_fit=TRUE)) %>%
  rename(pred_vs = .fitted, se_vs = .se.fit) %>%
  dplyr::select(pred_vs, se_vs)

merged_no_res <-  cbind(vp_df, vs_df) %>%
  mutate(se_vp_per = se_vp/pred_vp) %>%
  mutate(se_vs_per = se_vs/pred_vs) %>%
  filter(pred_vp >0, pred_vs >0) %>%
  filter(!is.na(se_vp), !is.na(se_vs)) %>%
  filter(se_vp_per < 0.1) %>%
  filter(se_vs_per < 0.1)

write_csv(merged_no_res, '../data/merged_tests_no_residual.csv')

This chunk merges the tests with residual stress results only.

vp_df <- upt_nest %>%
  inner_join(static_nest_w_res, by = c('well','round_depth','orientation')) %>%
  rename(upt_data = data.x, trx_data = data.y) %>%
  map2_df(.x = .$vp_model, .y = .$trx_data, 
          .f = ~augment(.x, newdata = .y, se_fit=TRUE)) %>%
  rename(pred_vp = .fitted, se_vp = .se.fit)

vs_df <- upt_nest %>%
  inner_join(static_nest_w_res, by = c('well','round_depth','orientation')) %>%
  rename(upt_data = data.x, trx_data = data.y) %>%
  map2_df(.x = .$vs_model, .y = .$trx_data, 
          .f = ~augment(.x, newdata = .y, se_fit=TRUE)) %>%
  rename(pred_vs = .fitted, se_vs = .se.fit) %>%
  dplyr::select(pred_vs, se_vs)

merged_w_res <- cbind(vp_df, vs_df) %>%
  mutate(se_vp_per = se_vp/pred_vp) %>%
  mutate(se_vs_per = se_vs/pred_vs) %>%
  filter(pred_vp >0, pred_vs >0) %>%
  filter(!is.na(se_vp), !is.na(se_vs)) %>%
  filter(se_vp_per < 0.1) %>%
  filter(se_vs_per < 0.1)

write_csv(merged_w_res, '../data/merged_tests_w_residual.csv')

ScottHMcKean/duvernay_geomech_model documentation built on Sept. 12, 2022, 10:08 a.m.