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Built a non-intrusive multiphase flow meter (MPFM) over two years (2016–2018)…

2026-08-06 · 23:28 UTC ·@Object_Zero_ ·3 min read

Brief

A non-intrusive multiphase flow meter system was developed by the author in 2016–2018 over two years using laser Doppler techniques and Michelson interferometers (dual SWIR, polarised fiber optics) after a ~£100k hardware outlay. Motivated by technicians listening to pipe sounds, the device measures pipe-surface velocity, transforms the signal with FFT into 3D spectrograms in 20 ms windows, and streams ~2 GB/s of data to a 36-node cluster where Matlab neural nets were trained. The processing pipeline is two-stage: a cluster-analysis classifier determines the flow regime (slugging, plugging, stratified, etc.), then regime-specific models estimate oil, water and gas mass flow rates. The author reports reaching ±7% accuracy within 2 days and ±2% within a week, argues this beats $5M radioactive MPFMs (~±20% accuracy), and claims real-time metering can raise oil output 7–9%, substantially increasing field profitability.

Why it matters

Built a non-intrusive multiphase flow meter (MPFM) over two years (2016–2018) using laser Doppler / Michelson interferometry hardware (dual SWIR interferometers, polarised fiber optics, lens heads, optical switches) after spending ~£100k on interferometers; instrument produced ~2 GB/s of empirical data.

Key details

  • Deployed a 36-node computer cluster and trained neural networks in Matlab on 3D spectrogram inputs derived from FFTs of pipe surface velocity in 20 ms time-averaged windows; calibration used a large water butt and pharmaceutical-grade scales to measure liquid out and separate scales to measure gas in.
  • Implemented a two-stage algorithm: (1) cluster-analysis classifier to detect flow regime (slugging, plugging, stratified, bubbly, elongated, wavy, etc.), then (2) regime-specific regression models for each phase (oil, water, gas); reported measurement accuracy of ±7% within 2 days and ±2% within a week.
  • Claims commercial context and impact: conventional radioactive MPFMs cost up to $5M and are ~±20% accurate; this non-intrusive MPFM yields real-time measurements (vs 20-minute separator lag), enabling claimed +7–9% more oil production and, with a 10% profit margin, an implied ~2x increase in profit/value.
Source evidence

Several year ago…

I built a non-intrusive multiphase flow meter system, this equipment was able to predict the flow regime and also the mass flow rates of each individual phase within a mixture of produced fluids (oil + gas + water + sand).

It tool me 2 years to get the technology working 2016-2018. There are a bunch of OFS companies who sell radioactive MPFM and those things might cost up to $5m a piece.

Why is this interesting?

Well if you have an oil well, it produces a volatile mixture of oil+gas+water+sand and it’s almost impossible to measure how much oil if flowing until you separate all those components out and you can measure single phase fluids only. To do that you have to use a separator and a separator takes 20 mins to separate the flows, you your data is always 20mins stale.

A multiphase flow meter tells you what your well is doing right now, so you can optimise choke and lift parameters in real time.

The result is +7-9% more oil gets produced, so 7-9% more revenue. If your profit margin is 10% then +9% means 2x profit and your entire oilfield is actually 2x as valuable.

So a multiphase flow meter that doesn’t cost $5m and doesn’t break 3 times a week is a valuable thing indeed.

So how the hell did I build one in 2 years? In 2016? and a NON-INTRUSIVE MFPM at that?

Pull up a chair, let’s dive in…

First of all any great oilfield production technician will walk up to a flowing pipe and press their hard plastic ear defender to the pipe, to listen to the flow inside the pipe.

Oil pipelines have a bunch of different flow regimes from slugging, plugging, stratified, bubbly, elongated, wavy, etc. These flow regimes all sound a bit different and you learn to recognise them over time.

So my first insight was “maybe a microphone can sense what the flow is doing?” and so I messed around with pick up mics for about a week before I landed on using a laser.

A laser is precise, and non-intrusive. A laser can bounce a beam off the pipe surface and measure the surface velocity (Doppler shift) and you can then unpack the sound.

After a few weeks I decided to spend £100k on a couple of Michelson Interferometers, I bought dual SWIR interferometers and polarised fiber optic and lens heads and optical switches and I built a system to bounce beams of pipes and measure the passive vibration in the pipe wall.

I built a flow loop in my workshop (will post pics below), and I built a 36 node computer cluster to build a cluster instance large enough to have enough VRAM so I could train neural nets (in Matlab!!).

My interferometer wrote 2GB/s of empirical data, and I collected calibrated flow data in my workshop and then trained neural nets (this pre-dated “Attention Is All You Need”).

I wrestled with algorithms for a long time, and eventually I got a two stage process that started to work.

I collected all the surface velocity data and put it through an FFT to produce a power frequency spectrum, and I stored that in time averaged 20ms windows and joined those up to created a 3D spectrogram (spectogram this was my input data), I also build a direct mass flow meter using a large water butt and some pharmaceutical grade scales (this was my target data).

I had scale that measured liquid out, and scales that measured gas in.

My algorithm

Stage 1: A classifier algorithm using cluster analysis would predict the flow regime.

Stage 2: I trained multiple models to predict each of the component phases (oil, water gas) within a specific flow regime. I selected the correct measurement model for the predicted flow regime. (I never managed to get a general measurement NN trained).

I manage to get down to +/- 7% accuracy within 2 days, and down to +/-2% accuracy within a week.

Bear in mind there are $50bn companies selling MPFM for $5m that are only +/- 20% accurate and they are gamma emitters!

The combination of interferometry + AI is extraordinarily powerful, I can’t imagine anything that can write empirical data as fast.