Copyright © 2005 POSC Integrated Operations Update David Archer, POSC Integrated Operations SIG Meeting Stavanger, Norway 18 November 2005

Oil fields of the future: real-time oil and gas operations Onshore Facilities Decision Centers Offshore Facilities new capabilities – much more data; much more exposure

Copyright © 2005 POSC Stavanger Bergen Trondheim Aberdeen Integrated operations External experts Real time data Service Company’s onshore operation centre Control room offshore External experts Remote collaboration room Operator’s onshore operation centre * Knowledge * Decisions * Actions * Data integration * Information * Visualisation

Copyright © 2005 POSC

Smart Systems The basic approach of all “smart technology” is measure-model-control –measure system properties –model actual vs desired behaviour –derive required correction parameters (adaptive control) –implement control ∆ IntOPS Acquire Model Analyze Control Source: Shell IntOPS = Integrated Operations

Copyright © 2005 POSC Business Headquarters Capacity Planning Design [months/years] Operational Planning [months/years] Scheduling [days/months] Supervisory Control [minutes/hours] Regulatory Control [sec/minutes] Well & Surface facilities -Flow, pressure and temperature in wells and separator -Fuel injection to produce heat out of a boiler - SCADA systems for coordinating flow stations and pipelines -Gas distribution/optimization on a pipeline network -Monitoring wellheads, multiples and flow stations -Scheduling of injection/production plan and resources - Opening and closing wells or partial completions - Adjusting well operating parameters -Planning of injection/production plan and resources - Planning drilling and workover resources - Supply Chain Management & Market and customer demands -Asset life cycle and installed based maintenance or growth -Supply Chain Management & Market and customer demands Automation level Time-scale Fast cycle Slower cycle Source: Saputelli SPE Time Scales (10 6 range) IntOPS Acquire Model Analyze Control

Copyright © 2005 POSC The Problem? Theorem 1 : 50% of the problems in the world result from people using the same words with different meanings Theorem 2 : The other 50% of the problems results from people using different words with the same meaning Stan Kaplan, Risk Analysis, Vol. 17, No. 4, 1997

Copyright © 2005 POSC Board Economics Reservoir Engineering Expl Geology PetrophysicsPetroleum Engineering Drilling Engineering Production Geology Production Engineering Facilities Engineering Production OperationsGeophysics Drilling Operations Completion & Workover E&P Catalogue Standards XML exchange standards, design guidelines, profiles Data Store Solutions SIG IntOPS SIG Reference Data Standards Industry Dictionary Business Objects eRegulatory SIG Practical Well Log Standards WellHeader, WellPath, WellLog, LogGraphics, Production Reporting, DTS, … NDR Meetings POSC Standards / SIGs Epicentre™ Data Model

Board Economics Reservoir Engineering Expl Geology PetrophysicsPetroleum Engineering Drilling Engineering Production Geology Production Engineering Facilities Engineering Production Operations Geophysics Drilling Operations Completion & Workover E&P Subject Areas: WITSML Coverage Can we use this image to portray the growth in WITSML coverage?

General Board Economics Reservoir Engineering Expl Geology PetrophysicsPetroleum Engineering Drilling Engineering Production Geology Production Engineering Facilities Engineering Production Operations Geophysics Drilling Operations Completion & Workover E&P Subject Areas: WITSML Coverage Enhancements for raw, calculated and planned Well Path data, coordinate systems, and units.  WellPath Well Wellbore Trajectory  CRS, Projections  Units of Measure

General Board Economics Reservoir Engineering Expl Geology PetrophysicsPetroleum Engineering Drilling Engineering Production Geology Production Engineering Facilities Engineering Production Operations Geophysics Drilling Operations Completion & Workover E&P Subject Areas: WITSML Coverage Enhancements for wireline as well as LWD log data.  Log,WellLog

General Board Economics Reservoir Engineering Expl Geology PetrophysicsPetroleum Engineering Drilling Engineering Production Geology Production Engineering Facilities Engineering Production Operations Geophysics Drilling Operations Completion & Workover E&P Subject Areas: WITSML Coverage Enhancements for Production beginning with Volume and Activity Reporting, continuing with PRODML towards Optimization.  Network Model Volume Report Activity Report

General Board Economics Reservoir Engineering Expl Geology PetrophysicsPetroleum Engineering Drilling Engineering Production Geology Production Engineering Facilities Engineering Production Operations Geophysics Drilling Operations Completion & Workover E&P Subject Areas: WITSML Coverage We have a pending request to establish a Geophysical SIG. Such a SIG could lead to WITSML Geophysical data exchange Standards.

General Board Economics Reservoir Engineering Expl Geology PetrophysicsPetroleum Engineering Drilling Engineering Production Geology Production Engineering Facilities Engineering Production Operations Geophysics Drilling Operations Completion & Workover E&P Subject Areas: WITSML Coverage At a future time, we could consider the viability of data exchange Standards for facility data, reservoir data, economics data, etc. filling out the E&P space.

General Board Economics Reservoir Engineering Expl Geology PetrophysicsPetroleum Engineering Drilling Engineering Production Geology Production Engineering Facilities Engineering Production Operations Geophysics Drilling Operations Completion & Workover E&P Subject Areas: WITSML Coverage This would call for close cooperation with other industry groups to avoid duplication of effort and ensure smooth transitions.

Copyright © 2005 POSC PRODML: A Shared Solution for Upstream Oil and Gas Companies to Optimize Their Production What? –Project focused on optimization of oil and gas production Who? –BP, Chevron, ExxonMobil, Shell, Statoil, eProduction Solutions, Halliburton, Invensys, OSIsoft, Petroleum Experts, Schlumberger, Sense Intellifield, TietoEnator and POSC What? –Production optimization involves integrating real time data from specialty, multi-vendor software applications and streamlining work processes to enable oil and gas field operational efficiencies –Build on success of WITSML™ - develop the necessary XML-based data exchange solutions as an open industry standard –Extend the WITSML™ ‘architecture’ to include data needed for field production optimization How? When? –Drive towards commercial software products to improve data exchange and work process efficiency in production optimization - over a 12 month timeframe –POSC will maintain the standard and make it publicly available

Joint Work of Richard Carter, Todd Dupont and Henry Rachford Control of Gas Flow in Pipelines Why is it Hard, What Can We Do about it, and Why do We Care?

Consider a Typical Pipe 60 Miles Long 47 inches Inside Diameter 1000 Microinches Roughness Pressure, p(t), at Inlet Flow, q(t), at Outlet, where t is time p(t) = Pin q(t)=Qout 60 miles, 47” inside diameter. InletOutlet

Consider a Simple Scenario

Settling q Toward a New State Steady t=0 Pin = 850 psia Qout = 1842 MMSCFD Temp = 60 o F Set Qout = 2090 Hold boundaries constant for t>0

Relaxation Time, t r. Let and δ (x,t) = p(x,t) - p(x,T), for 0 < x < L, t o < t < T. From any pipeline state fix p at inlet, q at outlet, to cause a state change. Then for T >> t o, we find L = Pipe Length c = Sonic Velocity in gas f = Friction coefficient t = A time after a change v =Gas velocity to=to= A fixed value of t d = Inside Diameter T=T=A very large value of t t r = 46.5 minutes, t o = 10 minutes

Introduce Sensors [ S(P), S(Q) ] L = Pipe Length c = Sonic Velocity in gas f = Friction coefficient t = A time after a change v =Gas velocity to=to= A fixed value of t d = Inside Diameter T=T=A very large value of t p(t) = Pin q(t)=Qout Inlet Outlet S(Pin),S(Qin)S(Pout),S(Qout)

What Does This Mean for a Real Pipeline System? Response to control takes hours Controls to Achieve One Goal may Interact Unexpectedly with Others Look at an Example

300-Mile 5-Station Pipeline p(t) q(t) 50 mi 40 mi 45 mi 55 mi 60 mi SP 1 (t) SP 2 (t) SP 3 (t) SP 4 (t) SP 5 (t) Station #Max HPMax Set PointMin. Suct. p psia 500 psia In Out Out04 q 4 (t)

Effects of Such Control Control of Station k Discharge, SP k (t), Implies Control of Pipe-End Flow, q k (t), on the Suction Side This Looks Like the 1-pipe Scenario Try a Scenario we Might Find in the Field

A Simple Scenario Steady State with OUT-End Flow 1500, Station 4 Delivery 450 (in MMSCFD) SP k (0)=800 psia, k=1,…,5 (in Control) Hold ‘IN’ at 825 psia (0:00 to 24:00). At 09:00 increase Delivery at OUT to 1950 MMSCFD for 5- Hours, return to 1500 Delivery at 24:00 is Same as at 00:00, so Make End State Same as Start State Use Simple Control: All Setpoints stay 800 Call this Scenario 1.

300-Mile 5-Station Pipeline p(t) q(t) 50 mi 40 mi 45 mi 55 mi 60 mi SP 1 (t) SP 2 (t) SP 3 (t) SP 4 (t) SP 5 (t) Station #Max HPMax Set PointMin. Suct. p psia 500 psia In Out Out04 q 4 (t) Qout4(t)=450Pin(t)=825 Qout(0-9)=1500; Qout(9-14)=1950; Qout(14-24)=1500 SP k (t)=800

Observations on Delivery 5-Hour Increase at OUT adds 450 MMSCFD. This Adds Same as Out04 Flow 450 Increase Cannot be Supported at Steady State (Max Steady Increase is 150) Total Initial Gas in Pipe: 1.07 BCF (Initial State Linepack) Extra Delivery is 93.7 MMSCF, or 8.7% of Linepack

Initial Pipeline State

Scenario 1: All Stations SP=800

Scenario 1: Station 1, 1 Day

Scenario 1: Station 2 Control Interchange

Scenario 1: Station 5, 1 Day

Scenario 1 Deliver OUT-End? Ouch!

Scenario 1: Final State Day’s End: 2.4% Loss in Linepack

Scenario 1: % Linepack Change

Scenario 1 Fails. What Should Be the Control? For this, Enumerate Goals: Make Deliveries above Minimum p Do not Violate Maximum Allowable p Do not Deplete the Pipeline, e.g., Achieve the End-of-Day Target State Always Maintain Control, i.e., Never Default Control to Station Maximum Minimize Fuel Usage, Emissions

How Can We Find Controls? Recall p(t), q(t), q 4 (t) are Specified by Users We Need to find SP k ( t ), k=1, 2, …, 5 Guess the five SP k ( t ) functions. Recall 900 PSIA is maximum value for any SP k ( t ) Simulate Results, Compare with Goals Use Trial and Error to Satisfy Goals With Experience, guessing and trial and error will improve

Alternatively, Guide the Trial and Error with Mathematics Define the Control Set S = SP k (t n ), k =1,…,5, n = 1,…,23, say, i.e., t n = n, i.e. 115 numbers Begin as Before: Take SP k (t n ) = 800 Interpolate in time between Control Values and use Interpolated Values to Simulate the Gas Day Quantify Missed Goals as a Functional, J Calculate Gradient of J with Respect to S Iterate to revise SP k (t n ), so as to Minimize J. Call Resulting Control Scenario 2

INITIAL STATE THE SAME

Scenario 2: External Flows

Scenario 2: Station 1 Performance

Scenario 2: Station 2 Performance

Scenario 2: Station 3 Performance

Scenario 2: Station 4 Performance

Scenario 2: Station 5 Performance

OUT-End Delivery?

Scenario 2: End-of-Day State Pack only 0.16% less than original

Scenario 2: Flows, % Pack Change

Compare Scenario Results 12

Conclusions Mathematical Optimization Finds a Control to Achieve Goals in 1-2 Minutes Clock Time If Delivery were Infeasible, this Fact Would have been Found just as Fast If Loads Change During Day, Simply find New Controls for the New Schedule again in 1-2 Minutes Clock Time

Questions? David Archer President / CEO POSC 24 Greenway Plaza Suite 1315 Houston, TX mobile