Presentation is loading. Please wait.

Presentation is loading. Please wait.

SPE 150370 Getting the Most out of Networked Drillstring Petersen, Sui, Frøyen, Nybø Center for Integrated Operations in the Petroleum Industry & SINTEF.

Similar presentations


Presentation on theme: "SPE 150370 Getting the Most out of Networked Drillstring Petersen, Sui, Frøyen, Nybø Center for Integrated Operations in the Petroleum Industry & SINTEF."— Presentation transcript:

1 SPE Getting the Most out of Networked Drillstring Petersen, Sui, Frøyen, Nybø Center for Integrated Operations in the Petroleum Industry & SINTEF Jackson, Veeningen, NOV IntelliServ

2 Agenda Downhole data through Networked Drillstrings Opportunity for modeling A proof of concept Conclusions and recommendations

3 Networked Drillstring LWD/MWD/RSS Tools Data measured at surface and in the BHA (MWD) Conditions along-string inferred or modeled BHA data limited by mud-pulse telemetry rates Almost impossible to accurately monitor entire wellbore in real-time Todays Wellbore Data

4 Networked Drillstring LWD/MWD/RSS Tools Interface SubNetworked Drillstring Distributed Sensors Increased bandwidth – via Networked tubulars (wired drill pipe) - bidirectional 56,000 bps Along string measurements technology Enhanced BHA measurements (density and quality Accurate & effective real-time decision making Along-String Distributed Measurements

5 Networked Drillstring LWD/MWD/RSS Tools Interface SubNetworked Drillstring Distributed Sensors Real-time wellbore sensor arrays

6 Using distributed sensor data Need a model to interpret the data and to see the implications Expect distributed sensor data to: – Provide redundancy – Improve accuracy – Reveal new phenomena Most models are designed around measurements at the top and bottom only

7 Networked Drillstring + Advanced Dynamic Drilling Simulator Drilling simulator: – High resolution parameters (fine spatial grid) – Small timesteps – Dynamic 2-D temperature model Measurements – Direct: Pressure and Temperature – By combining model and measurements: Mud densities, cuttings density, cuttings loading, reservoir fluid type and densities, slip relation, fluid viscosities, wall roughness, heat capacity and conduction, background temperatures, etc.

8 Divide and Conquer? "Nearly independent" parameters No Flow P(h) = g h cos + P top – Integrated density – between measurements Densities (P,T) information obtained at previous measurements – Local temperature measurements Other temperature information given at previous measurements – From temperature curves – Temperature vs. time Obtain detailed formation background temperature

9 Divide and Conquer? "Nearly independent" parameters Flow P(h) = g h cos + P fric + P top – Integrated density – fairly well known from previous measurements – Flow velocity fairly well known from diameters and pump rate – Viscosity and wall roughness can be obtained from P fric

10 High rate data acquisition – Model matching: Reliable parameter space Deviations Model data & Measurements mismatch Causes Cuttings loadingOpen hole washout Kick Wellbore breathing/Loss of circulation Measurement error Etc. Each deviation has a separate "fingerprint" and can be discerned using appropriate software.

11 Experiment A real-time drilling simulator has been developed by SINTEF The simulator was altered to output data as it would appear from a fictional drilling operation rich in distributed sensors The "simulated sensor readings" were input to a simplified wellbore model predicting BHP. The simple model was altered to make use of distributed data

12 Simulated operation: Pumping 200 l/min for 5 minutes, then stop. Lowering the bit and tag bottom Start drilling, pumping 1000 l/min for 60 minutes, drilling at 20 m/hr. Circulate clean for 60 minutes

13 Model strategy 1.Parameter search in old model for best match with BHP 2.Estimate pressure at sensor 1 along string 3.If match with sensor, save model parameters 4.Work backwards from sensor 1 to estimate BHP. 5.Repeat for sensor 2,3,.. 6.Combine BHP-estimates Model OK? MeasurementModel estimate

14 Model strategy MeasurementModel estimate OK? New model parameter calibrated! More accurate and reliable BHP prediction

15

16

17 Conclusions By combining redundant measurements in space and time, we can calibrate the model and get: – A detailed view of the situation along the whole well – Predictive power for the whole well – Safety by redundancy The flow around the BHA is a complicating factor – Difficult to calculate BHP from pressure above BHA and vice versa – Parameters remain uncertain to some degree, since the sensors don't slide past the BHA components.

18 Recommendations Design simulators and that make use of parallel processing (model tuning & high bandwidth) Simulate sensor configurations w.r.t: redundancy, accuracy and ability to detect drilling problems Consider multiple sensors along the BHA – More robust and accurate BHP-measurements – Hole-cleaning problems visible in high resolution – Especially relevant for MPD and long open-hole sections

19 Slide 19 of 5 Thank you


Download ppt "SPE 150370 Getting the Most out of Networked Drillstring Petersen, Sui, Frøyen, Nybø Center for Integrated Operations in the Petroleum Industry & SINTEF."

Similar presentations


Ads by Google