Mesa rotatoria
Leonardo Ruiz
2026-03-08
knitr::opts_chunk$set(echo = TRUE)
setwd("C:/Users/LEO/Documents/ESTA")
Datos <- read.csv("tabela_de_pocos_janeiro_2018.csv", header = TRUE, sep = ";" , dec = ".", fileEncoding = "Latin1")1 Introducción y Metodología
El presente informe estadístico analiza la variable Cota Altimétrica de pozos petroleros de Brasil.
library(readxl)
library(dplyr)
library(gt)
library(e1071)
Datos_Brutos <- read_xlsx("C:/Users/LEO/Documents/ESTA/tabela_de_pocos_janeiro_2018.xlsx", sheet = 1)
colnames(Datos_Brutos) <- trimws(colnames(Datos_Brutos))
Datos <- Datos_Brutos %>%
select(any_of(c("POCO", "MESA_ROTATIVA"))) %>%
mutate(Variable_Analisis = as.numeric(gsub(",", ".", as.character(MESA_ROTATIVA))))
Variable <- na.omit(Datos$Variable_Analisis)
# Filtro para valores razonables (0 a 1000 metros)
Variable <- Variable[Variable >= 0 & Variable < 1000]
if(length(Variable) == 0) {
stop("ERROR: No hay datos válidos para la variable seleccionada.")
}
# 2. CÁLCULOS MATEMÁTICOS PARA LA TABLA
N <- length(Variable)
K <- floor(1 + 3.322 * log10(N))
breaks_table <- seq(min(Variable), max(Variable), length.out = K + 1)
# Cálculo de ni usando cut
ni <- as.vector(table(cut(Variable, breaks = breaks_table, include.lowest = TRUE, right = FALSE)))
# Cálculo de vectores estadísticos (Mantenemos los valores reales sin redondear para la tabla)
hi <- (ni / sum(ni)) * 100
Ni_asc <- cumsum(ni)
Ni_desc <- rev(cumsum(rev(ni)))
Hi_asc <- cumsum(hi)
Hi_desc <- rev(cumsum(rev(hi)))
# Creación de la Tabla de Distribución de Frecuencias (TDF)
TDF_Mesa <- data.frame(
Li = round(breaks_table[1:K], 2),
Ls = round(breaks_table[2:(K+1)], 2),
MC = round((breaks_table[1:K] + breaks_table[2:(K+1)]) / 2, 2),
ni = ni,
hi = hi, # <- CAMBIO AQUÍ: Pasamos el valor real sin la función round() para evitar que se acumulen decimales erróneos
Ni_asc = Ni_asc,
Ni_desc = Ni_desc,
Hi_asc = Hi_asc,
Hi_desc = Hi_desc
)2 Distribución de Frecuencias
Tabla de distribución de frecuencias para la Cota Altimétrica.
TDF_Mesa %>%
gt(rowname_col = "Li") %>%
tab_header(
title = md("**DISTRIBUCIÓN DE FRECUENCIAS: ELEV_MESA_ROT**"),
subtitle = md("Variable: **elev_mesa_rot**")
) %>%
tab_source_note(source_note = "Fuente: Datos ANP 2018") %>%
grand_summary_rows(
columns = c(ni, hi),
fns = list("TOTAL" = ~sum(., na.rm = TRUE))
) %>%
# Formateamos números enteros
fmt_number(
columns = c(ni, Ni_asc, Ni_desc),
decimals = 0,
use_seps = TRUE
) %>%
# Formateamos los porcentajes a 2 decimales
fmt_number(
columns = c(hi, Hi_asc, Hi_desc),
decimals = 2
) %>%
cols_label(
Ls = "Lím. Sup", MC = "Marca Clase (Xi)",
ni = "ni", hi = "hi (%)",
Ni_asc = "Ni (Asc)", Ni_desc = "Ni (Desc)",
Hi_asc = "Hi (Asc)", Hi_desc = "Hi (Desc)"
) %>%
tab_stubhead(label = "Lím. Inf") %>%
# Alineación de datos al centro
cols_align(align = "center", columns = everything()) %>%
# Alineación del Stub (Lím. Inf y la palabra TOTAL) al centro
tab_style(
style = cell_text(align = "center"),
locations = cells_stub()
) %>%
# Estética de los encabezados (Azul oscuro / Verde petróleo con texto blanco)
tab_style(
style = list(cell_fill(color = "#1F4E5B"), cell_text(color = "white", weight = "bold")),
locations = list(cells_title(), cells_column_labels(), cells_stubhead())
) %>%
tab_options(
table.border.top.style = "none",
table.border.bottom.color = "#2E4053",
column_labels.border.bottom.color = "#2E4053",
data_row.padding = px(6)
)| DISTRIBUCIÓN DE FRECUENCIAS: ELEV_MESA_ROT | ||||||||
| Variable: elev_mesa_rot | ||||||||
| Lím. Inf | Lím. Sup | Marca Clase (Xi) | ni | hi (%) | Ni (Asc) | Ni (Desc) | Hi (Asc) | Hi (Desc) |
|---|---|---|---|---|---|---|---|---|
| 0.00 | 66.27 | 33.13 | 20,359 | 70.60 | 20,359 | 28,838 | 70.60 | 100.00 |
| 66.27 | 132.53 | 99.40 | 6,242 | 21.65 | 26,601 | 8,479 | 92.24 | 29.40 |
| 132.53 | 198.80 | 165.67 | 1,531 | 5.31 | 28,132 | 2,237 | 97.55 | 7.76 |
| 198.80 | 265.07 | 231.93 | 403 | 1.40 | 28,535 | 706 | 98.95 | 2.45 |
| 265.07 | 331.33 | 298.20 | 65 | 0.23 | 28,600 | 303 | 99.17 | 1.05 |
| 331.33 | 397.60 | 364.47 | 69 | 0.24 | 28,669 | 238 | 99.41 | 0.83 |
| 397.60 | 463.87 | 430.73 | 22 | 0.08 | 28,691 | 169 | 99.49 | 0.59 |
| 463.87 | 530.13 | 497.00 | 19 | 0.07 | 28,710 | 147 | 99.56 | 0.51 |
| 530.13 | 596.40 | 563.27 | 25 | 0.09 | 28,735 | 128 | 99.64 | 0.44 |
| 596.40 | 662.67 | 629.53 | 21 | 0.07 | 28,756 | 103 | 99.72 | 0.36 |
| 662.67 | 728.93 | 695.80 | 19 | 0.07 | 28,775 | 82 | 99.78 | 0.28 |
| 728.93 | 795.20 | 762.07 | 15 | 0.05 | 28,790 | 63 | 99.83 | 0.22 |
| 795.20 | 861.47 | 828.33 | 27 | 0.09 | 28,817 | 48 | 99.93 | 0.17 |
| 861.47 | 927.73 | 894.60 | 14 | 0.05 | 28,831 | 21 | 99.98 | 0.07 |
| 927.73 | 994.00 | 960.87 | 7 | 0.02 | 28,838 | 7 | 100.00 | 0.02 |
| TOTAL | — | — | 28838 | 100 | — | — | — | — |
| Fuente: Datos ANP 2018 | ||||||||
3 Análisis Gráfico
col_gris_azulado <- "#5D6D7E"
col_ejes <- "#2E4053"
max_var <- max(Variable)
breaks_50 <- seq(0, max_var + 50, by = 50)
h_base <- hist(Variable, breaks = breaks_50, plot = FALSE)
limite_x <- c(0, max(max_var, 100)) 3.1 GRÁFICO 1: Histograma Absoluto
par(mar = c(8, 5, 4, 2))
plot(h_base,
main = "Gráfica No.1: Distribución de elev_mesa_rot de Pozos Petroleros de Brasil",
xlab = "Mesa Rotatoria - elev_mesa_rot (m)", ylab = "Frecuencia Absoluta",
col = col_gris_azulado, border = "white", axes = FALSE,
ylim = c(0, max(h_base$counts) * 1.1),
xlim = limite_x)
axis(1, at = seq(0, limite_x[2], by = 50), las = 2, cex.axis = 0.7)
axis(2)
grid(nx = NA, ny = NULL, col = "#D7DBDD", lty = "dotted") 
3.2 GRÁFICO 2: Histograma Global
par(mar = c(8, 5, 4, 2))
plot(h_base,
main = "Gráfica N°2: Distribución de elev_mesa_rot de Pozos Petroleros de Brasil",
xlab = "Mesa Rotatoria - elev_mesa_rot (m)", ylab = "Total Pozos",
col = col_gris_azulado, border = "white", axes = FALSE,
ylim = c(0, sum(h_base$counts)),
xlim = limite_x)
axis(1, at = seq(0, limite_x[2], by = 50), las = 2, cex.axis = 0.7)
axis(2)
grid(nx = NA, ny = NULL, col = "#D7DBDD", lty = "dotted")
3.3 GRÁFICO 3: Porcentajes (Local)
h_porc <- h_base
h_porc$counts <- (h_porc$counts / sum(h_porc$counts)) * 100
par(mar = c(8, 5, 4, 2))
plot(h_porc,
main = "Gráfica N°3: Distribución Porcentual de elev_mesa_rot de Pozos Petroleros de Brasil",
xlab = "Mesa Rotatoria - elev_mesa_rot (m)", ylab = "Porcentaje (%)",
col = col_gris_azulado, border = "white", axes = FALSE, freq = TRUE,
ylim = c(0, max(h_porc$counts)*1.2),
xlim = limite_x)
axis(1, at = seq(0, limite_x[2], by = 50), las = 2, cex.axis = 0.7)
axis(2)
text(x = h_base$mids[h_base$mids <= limite_x[2]],
y = h_porc$counts[h_base$mids <= limite_x[2]],
label = paste0(round(h_porc$counts[h_base$mids <= limite_x[2]], 1), "%"),
pos = 3, cex = 0.6, col = col_ejes)
3.4 GRÁFICO 4: Global Porcentual
par(mar = c(8, 5, 4, 2))
plot(h_porc,
main = "Gráfica No.4: Distribución Porcentual de elev_mesa_rot de Pozos Petroleros de Brasil",
xlab = "Mesa Rotatoria - elev_mesa_rot (m)", ylab = "% del Total",
col = col_gris_azulado, border = "white", axes = FALSE, freq = TRUE,
ylim = c(0, 100),
xlim = limite_x)
axis(1, at = seq(0, limite_x[2], by = 50), las = 2, cex.axis = 0.7)
axis(2)
text(x = h_base$mids[h_base$mids <= limite_x[2]],
y = h_porc$counts[h_base$mids <= limite_x[2]],
label = paste0(round(h_porc$counts[h_base$mids <= limite_x[2]], 1), "%"),
pos = 3, cex = 0.6, col = col_ejes)
4 Diagrama de Caja y Ojivas
4.1 GRÁFICO 5: Boxplot
col_gris_azulado <- "#5D6D7E"
col_acento <- "#C0392B"
par(mar = c(8, 5, 4, 2))
boxplot(Variable, horizontal = TRUE, col = col_gris_azulado,
main = "Gráfica No.5: Diagrama de Caja (elev_mesa_rot)",
xlab = "Mesa Rotatoria - elev_mesa_rot (m)", outline = TRUE, outpch = 19,
outcol = col_acento, axes = FALSE, xlim = c(0.7, 1.3),
ylim = limite_x)
axis(1, at = seq(0, limite_x[2], by = 50), las = 2, cex.axis = 0.7)
box()
4.2 GRÁFICO 6: Ojivas
col_azul_oscuro <- "#2E4053"
col_rojo_fuerte <- "#C0392B"
par(mar = c(8, 5, 4, 8), xpd = TRUE)
x_vals_ojiva <- breaks_table
plot(x_vals_ojiva, c(0, Ni_asc), type = "o", col = col_azul_oscuro,
lwd=2, pch=19, axes=F,
main = "Gráfica No.6: Ojivas Ascendente y Descendente (elev_mesa_rot)",
xlab = "Mesa Rotatoria - elev_mesa_rot (m)", ylab = "Frecuencia acumulada")
lines(x_vals_ojiva, c(Ni_desc, 0), type = "o", col = col_rojo_fuerte,
lwd=2, pch=19)
axis(1, at = seq(0, max(breaks_table), by = 50), las = 2, cex.axis = 0.6)
axis(2)
legend("right", legend = c("Ascendente", "Descendente"),
col = c(col_azul_oscuro, col_rojo_fuerte),
lty = 1, pch = 19, cex = 0.7, lwd=2,
inset = c(-0.15, 0), bty="n")
grid()
5 Resumen Estadístico
media_val <- mean(Variable)
mediana_val <- median(Variable)
sd_val <- sd(Variable)
status_atipicos <- if(length(boxplot.stats(Variable)$out) > 0) {
paste0(length(boxplot.stats(Variable)$out), " [", round(min(boxplot.stats(Variable)$out), 2), "; ", round(max(boxplot.stats(Variable)$out), 2), "]")
} else { "0 (Sin atípicos)" }
df_resumen <- data.frame(
Variable = "elev_mesa_rot",
Rango = paste0("[", round(min(Variable), 2), "; ", round(max(Variable), 2), "]"),
Media = media_val,
Mediana = mediana_val,
Moda = paste(round(TDF_Mesa$MC[TDF_Mesa$hi == max(TDF_Mesa$hi)], 2), collapse = ", "),
Varianza = var(Variable),
Desv_Std = sd_val,
CV_Porc = (sd_val / abs(media_val)) * 100,
Asimetria = skewness(Variable, type = 2),
Curtosis = kurtosis(Variable, type = 2),
Atipicos = status_atipicos
)
df_resumen %>%
gt() %>%
tab_header(title = md("CONCLUSIONES Y ESTADÍSTICOS"), subtitle = "Variable: elev_mesa_rot") %>%
fmt_number(columns = c(Media, Mediana, Varianza, Desv_Std, CV_Porc, Curtosis), decimals = 2) %>%
fmt_number(columns = Asimetria, decimals = 4) %>%
cols_width(
Variable ~ px(140),
Rango ~ px(110),
Moda ~ px(100),
Atipicos ~ px(140),
everything() ~ px(85)
) %>%
tab_options(column_labels.background.color = "#2E4053") %>%
tab_style(style = list(cell_text(weight = "bold", color = "white")), locations = cells_column_labels())| CONCLUSIONES Y ESTADÍSTICOS | ||||||||||
| Variable: elev_mesa_rot | ||||||||||
| Variable | Rango | Media | Mediana | Moda | Varianza | Desv_Std | CV_Porc | Asimetria | Curtosis | Atipicos |
|---|---|---|---|---|---|---|---|---|---|---|
| elev_mesa_rot | [0; 994] | 56.16 | 32.00 | 33.13 | 4,829.68 | 69.50 | 123.76 | 5.4490 | 49.24 | 1269 [164.79; 994] |
6 Conclusiones
min_txt <- format(min(Variable), scientific = FALSE)
max_txt <- format(max(Variable), scientific = FALSE)
asimetria_val <- skewness(Variable, type = 2)
centro_valor <- format(round(if(abs(asimetria_val) > 0.5) median(Variable) else mean(Variable), 2), scientific = FALSE)
cv_calc <- (sd(Variable) / abs(mean(Variable))) * 100
tipo_homogeneidad <- if(cv_calc > 30) "heterogénea" else "homogénea"
donde_se_concentra <- if(asimetria_val > 0) "valores bajos" else "valores altos"
juicio_operativo <- if(median(Variable) < 300) "dentro del estándar operacional" else "requiere equipos de mayor capacidad"
cat(paste0(
"## Análisis de elev_mesa_rot\n\n",
"La variable **elev_mesa_rot** oscila entre **", min_txt, "** y **", max_txt, "** metros. ",
"El centro de la distribución se localiza en **", centro_valor, "** metros. ",
"La muestra se define como una variable **", tipo_homogeneidad, "** (CV: ", round(cv_calc, 2), "%), ",
"presentando una mayor densidad en los **", donde_se_concentra, "** de la escala. ",
"Desde el punto de vista de ingeniería, estos valores se consideran **", juicio_operativo, "** para las operaciones de perforación."
))6.1 Análisis de elev_mesa_rot
La variable elev_mesa_rot oscila entre 0 y 994 metros. El centro de la distribución se localiza en 32 metros. La muestra se define como una variable heterogénea (CV: 123.76%), presentando una mayor densidad en los valores bajos de la escala. Desde el punto de vista de ingeniería, estos valores se consideran dentro del estándar operacional para las operaciones de perforación.