Ayomide242/Reservoir-Quality-Analysis

Petrophysical analysis of Niger Delta well log data using Python, featuring facies discrimination, Reservoir Quality Index (RQI) engineering, and Net Pay quantification with a 40.24% NTG result. # Reservoir Quality Analysis — GCA Capstone Project A data-driven petrophysical analysis of well log data to identify and quantify the best-quality reservoir intervals in a Niger Delta well. --- ## Project Objective This project focuses on identifying the best quality rock (reservoir) using data logic applied to wireline log data from **Well A** (a Niger Delta well). By combining statistical facies discrimination, feature engineering, and boolean filtering, the analysis delivers a quantified Net Pay result that can directly inform field development decisions. --- ## Workflow 1. **Import Libraries**: `lasio`, `numpy`, `pandas`, `matplotlib`, `seaborn` 2. **Dataset Loading & Cleaning**: Load `.LAS` file, handle null values, inspect well metadata and curve information 3. **Statistical Facies Discrimination**: Gamma Ray histogram analysis to justify and select GR cutoffs 4. **Feature Engineering**: Compute the Reservoir Quality Index (RQI) 5. **Net Pay Quantification**: Apply pay sand criteria and calculate gross thickness, net pay, and NTG --- ## Key Results | Metric | Value | |---|---| | Reservoir Gross Thickness | ~941.98 m | | Net Pay Thickness | ~379.02 m | | Net-to-Gross (NTG) | **40.24%** | --- ## Methods & Techniques ### GR Cutoffs (Niger Delta Context) The Gamma Ray histogram reveals a **bimodal distribution** consistent with interbedded sand-shale sequences of the **Agbada Formation**. Two cutoffs were evaluated: - **60 API** (industry standard) — conservative cutoff for clean sand discrimination - **75 API** (high-side) — captures marginal sands in the transition zone ### Reservoir Quality Index (RQI) A custom feature engineered to move beyond simple lithology identification: ``` RQI = Porosity × (1 − GR_norm) ``` GR values are normalised to a 0–1 range (0 = cleanest sand, 1 = purest shale), making RQI a dimensionless index that highlights intervals that are **both clean and porous** — the most productive sands. ### Net Pay Criteria Pay sands …