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File size: 2,738 Bytes
8c914be | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | from pathlib import Path
import pandas as pd
import streamlit as st
# Absolute path — required for HuggingFace Spaces Docker environment
_CSV_PATH = Path(__file__).parent.parent / "Final PF-12 and Injection.csv"
# Key columns used by the app
DISPLAY_COLUMNS = [
"DATEPRD", "BORE_OIL_VOL", "BORE_GAS_VOL",
"BORE_WAT_VOL", "AVG_WHP_P", "AVG_CHOKE_SIZE_P", "ON_STREAM_HRS",
]
@st.cache_data
def load_data() -> pd.DataFrame:
"""
Load and clean the Volve production CSV.
- Parses DATEPRD with explicit format='%d-%b-%y' (e.g. 01-Sep-07)
- Filters to ON_STREAM_HRS > 0 (removes 404 shut-in rows)
- Adds WATER_CUT derived column
- Sorts by DATEPRD ascending
Returns a DataFrame with 2901 producing-day rows.
"""
df = pd.read_csv(_CSV_PATH)
df["DATEPRD"] = pd.to_datetime(df["DATEPRD"], format="%d-%b-%y")
# Sanity check date range (Volve dataset is Sept 2007 – Sept 2016)
assert df["DATEPRD"].min().year == 2007, "Date parse error: min year != 2007"
assert df["DATEPRD"].max().year == 2016, "Date parse error: max year != 2016"
df = df.sort_values("DATEPRD").reset_index(drop=True)
# Remove shut-in days — ON_STREAM_HRS == 0 rows corrupt DCA fitting
df = df[df["ON_STREAM_HRS"] > 0].copy()
# Derived column: water cut (fraction), bounded [0, 1]
total_liquid = df["BORE_OIL_VOL"] + df["BORE_WAT_VOL"]
df["WATER_CUT"] = df["BORE_WAT_VOL"] / total_liquid.replace(0, float("nan"))
df["WATER_CUT"] = df["WATER_CUT"].clip(0.0, 1.0)
return df.reset_index(drop=True)
def filter_by_date(df: pd.DataFrame, start, end) -> pd.DataFrame:
"""
Return rows where DATEPRD is within [start, end] inclusive.
start and end may be datetime.date or pd.Timestamp objects.
Does NOT mutate the input DataFrame (no inplace=True).
"""
mask = (df["DATEPRD"] >= pd.Timestamp(start)) & (df["DATEPRD"] <= pd.Timestamp(end))
return df.loc[mask].copy()
def get_sidebar_kpis(df: pd.DataFrame) -> dict:
"""
Return dataset-level KPIs for the persistent sidebar display.
All pages call this to populate sidebar info cards.
Returns dict with: well_name, date_start, date_end, total_oil_mmsm3, peak_rate, peak_date, on_stream_days
"""
total_oil = df["BORE_OIL_VOL"].sum() # Sm³
peak_idx = df["BORE_OIL_VOL"].idxmax()
return {
"well_name": "15/9-F-12",
"date_start": df["DATEPRD"].min().strftime("%b %Y"),
"date_end": df["DATEPRD"].max().strftime("%b %Y"),
"total_oil_mmsm3": round(total_oil / 1_000_000, 3),
"peak_rate": int(df.loc[peak_idx, "BORE_OIL_VOL"]),
"peak_date": df.loc[peak_idx, "DATEPRD"].strftime("%d %b %Y"),
"on_stream_days": len(df),
}
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