Drilling Mud Rheology Study of Water-Based Bentonite Muds

Drilling Mud Rheology Study of Water-Based Bentonite Muds is a laboratory measurement project that prepares a family of water-based drilling muds from commercially available bentonite, measures their flow behaviour as a mud engineer would on a rig, and fits the results to the standard rheology models — Bingham plastic, power law and Herschel–Bulkley — so the numbers can be used for hole-cleaning and ECD reasoning.

Drilling mud is the working fluid of every well: it carries cuttings, cools the bit, stabilises the borehole wall and controls formation pressure. Whether it can depends on its rheology — plastic viscosity (PV), yield point (YP), gel strengths and funnel viscosity. On rigs in the Krishna–Godavari basin, mud engineers check these daily with a Marsh funnel and a six-speed viscometer following API RP 13B-1. In college, students usually only see the formulas PV = θ600 − θ300 and YP = θ300 − PV solved on paper.

This project closes that gap with a full measurement campaign. The team mixes muds at 4%, 6% and 8% bentonite, each with and without 0.2% PAC polymer, hot-rolls and ages them for 24 hours, and measures density, funnel viscosity, dial readings at all six speeds, and 10-second/10-minute gel strengths, repeating at three temperatures. Python scripts fit the three rheology models to the dial readings, compare fit quality, and show how bentonite concentration, polymer treatment and temperature each move PV, YP and gel strength — a small, rigorous version of the decisions a drilling engineer makes daily.

Water-based drilling muds carry cuttings, cool the bit and control formation pressure, and their performance is governed by measurable rheological properties — plastic viscosity, yield point, gel strengths and the flow-curve parameters n and K. Rig crews verify these properties daily using API RP 13B-1 procedures, yet petroleum-engineering graduates often arrive on site having only solved the Bingham formulas in an exam, with no feel for how formulation changes move those numbers or how well the textbook models actually represent a real mud.

There is a need for a systematic, reproducible laboratory study that prepares water-based bentonite muds at defined concentrations and polymer dosages, measures their rheology with the same instruments and procedures used on rigs, fits the results to the Bingham-plastic, power-law and Herschel–Bulkley models, and interprets the trends in terms of hole cleaning, suspension and pumping decisions — so that the student learns mud rheology as a measured engineering quantity rather than a formula.

Abstract

This project measures and models the rheology of water-based bentonite drilling muds at three concentrations, with and without polymer treatment, across three temperatures, following API RP 13B-1 field-test practice. The deliverables are a complete rheological dataset, fitted Bingham-plastic, power-law and Herschel–Bulkley parameters, and a discussion connecting the measured trends to hole-cleaning and suspension requirements.

Introduction

A drilling fluid must be thin enough to pump, thick enough to carry cuttings when circulation stops, and stable enough to suspend weighting material for hours. These opposing demands are tuned through rheology: the plastic viscosity reflects mechanical friction between particles and the liquid phase, the yield point reflects the electrochemical attraction structure that carries cuttings, and gel strength shows how quickly and strongly that structure rebuilds. Mud engineers balance these daily by adjusting bentonite content, polymer dosage and pH.

Existing Practice and Its Limits

• Field measurement is standardised (Marsh funnel time, six-speed dial readings, gel strengths under API RP 13B-1), but college teaching usually compresses it to two formulas and one solved example.
• Published mud literature reports values for specific commercial systems whose formulation is proprietary, so students cannot reproduce them.
• Few student studies fit all three rheological models to the same dataset and compare which model represents bentonite mud better, or examine the temperature sensitivity that matters on deep wells.
• Limitation: no accessible, reproducible campaign links formulation → measurement → model fitting → drilling-practice interpretation.

Proposed System

• A formulation matrix: 4%, 6% and 8% (w/v) bentonite, each untreated and treated with 0.2% PAC, mixed with high-shear stirring, hydrated for 24 hours after hot-rolling at 65 °C.
• A measurement campaign per sample: density (mud balance), Marsh funnel time, dial readings θ600, θ300, θ200, θ100, θ6, θ3, and gel strengths at 10 s and 10 min; the whole matrix repeated at 40 °C, 60 °C and 80 °C using a hot-water bath.
• A modelling step in Python: PV, YP and apparent viscosity from Bingham relations; flow-behaviour index n and consistency index K from the 600/300 pair; a SciPy least-squares fit of the Herschel–Bulkley curve to all six dial points; R² comparison across models.
• An interpretation chapter that reads each trend the way a mud engineer would: what a rising YP/PV ratio does to hole cleaning, why 10-minute gel strength that is too high causes pump-pressure spikes when circulation restarts, and where polymer treatment buys equivalent suspension at lower viscosity.

Feasibility

• Technical: the instruments are standard in any petroleum-engineering department's drilling-fluids lab; the mixing, ageing and measurement procedures are explicitly documented in API RP 13B-1, and the fitting is undergraduate mathematics.
• Economic: bentonite, PAC, caustic soda and measuring consumables total roughly ₹4,000–6,000; no instrument purchase is required.
• Operational: the campaign fits the 14-week academic window because samples are prepared in parallel batches and each measurement session takes under two hours.

In scope

• Fresh water-based bentonite muds with PAC treatment and barite weighting to one demonstration density (10 ppg).
• Laboratory measurement with the department's Marsh funnel, six-speed viscometer, mud balance and hot-water bath, following API RP 13B-1 procedures.
• Temperature series from 40 °C to 80 °C at atmospheric pressure.
• Rheological model fitting in Python and engineering interpretation of trends.

Out of scope

• Oil-based or synthetic-based muds and invert emulsions.
• High-pressure/high-temperature rheometry (HPHT viscometer) — pressure effects are discussed qualitatively only.
• Filtration (API filter press) and shale-inhibition testing, though a single demonstration filtrate test is included for completeness.
• Contaminant studies (cement, salt, calcium) beyond one demonstration gypsum contamination run.

The study follows a factorial measurement campaign with a formal modelling stage, spread over the two VTU project phases.

Testing approach: instrument-level control (viscometer zero check with blank water, spring-tension verification per the manual), procedural control (fixed shear schedule, fixed pre-stir, repeat readings averaged), and data-level control (any dial pair deviating more than the instrument's repeatability from its batch mean triggers a re-measurement). Uncertainties are propagated into the fitted parameters so the model comparison is honest.

The study is organised as a formulation → measurement → modelling → interpretation pipeline, drawn below as the study-design flowchart; the sampling matrix and the analysis scripts mirror it directly.

flowchart TD
  M{"Formulation matrix"}
  M -->|"4 / 6 / 8 percent bentonite"| MIX["High-shear mixing, pH to 9-10"]
  M -->|"0.2 percent PAC treated and untreated"| MIX
  MIX --> HR["Hot-roll at 65 C, age 24 h"]
  HR --> T{"Temperature series 40 / 60 / 80 C"}
  T --> FV["Marsh funnel time"]
  T --> MB["Density - mud balance"]
  T --> V["Six-speed dial readings"]
  T --> G["Gel strength 10 s and 10 min"]
  FV --> D["Raw datasheets"]
  MB --> D
  V --> D
  G --> D
  D --> PY["Python: Bingham PV/YP, power-law n and K, Herschel-Bulkley fit"]
  PY --> CMP{"Model comparison by R-squared"}
  CMP --> INT["Interpretation: hole cleaning, suspension, ECD"]
  INT --> REP["Phase-II report and viva"]

Prose description. The left half of the pipeline is chemistry: each formulation is mixed under fixed shear and time so that samples differ only in composition, then aged identically. The right half is physics: every property is measured under a fixed thermal state, and the Python stage converts raw dial readings into model parameters with stated uncertainties. The final interpretation step is deliberately last — no trend is discussed until its dataset passes the repeat-reading check, which is the same discipline a mud report follows before a recommendation is issued.

• Filtration and inhibition extension: pair the rheology matrix with API filter-press tests and a simple shale-inhibition screen so the mud programme is evaluated on both flow and wall-building behaviour.
• Contamination suite: systematic cement, salt and calcium-carbonate contamination with treatment schedules (soda ash, SAPP) measured end to end.
• HPHT trend study: collaborate with a nearby institute that has an HPHT viscometer to extend the temperature series under pressure.
• Optimisation: formulate a target PV/YP window from hole-cleaning correlations and search the formulation space experimentally for the cheapest mix that meets it.
• Reproducibility pack: publish the cleaned dataset and fitting scripts so successive batches can benchmark against your campaign.