OTC 14009 Deep Offshore Well Metering and Permutation Testing
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OTC 14009 Deep Offshore Well Metering and Permutation Testing

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This paper presents two...

This paper presents two
complementary methodologies for operation support and
improvement of the production conditions. The first one is
based on data reconciliation between process measurements
and flow modelling. It brings an additional level of
information to the problem of continuous metering of deepwater
subsea wells. As periodic well testing is required to
achieve this predictive metering, the second methodology
provides the optimal test sequences of well permutations. It
involves flow process simulation and algorithmical sorting,
according to production constraints and operating strategies.

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OTC 14009 Deep Offshore Well Metering and Permutation Testing OTC 14009 Deep Offshore Well Metering and Permutation Testing Document Transcript

  • OTC 14009 Deep Offshore Well Metering and Permutation Testing Erich Zakarian, RSI; Arnaud Constant, TotalFinaElf Exploration & Production Angola; Lionel Thomas, TotalFinaElf; Martin Gainville, Institut Français du Pétrole; Pierre Duchet-Suchaux, TotalFinaElf; and Philippe Grenier, RSI Copyright 2002, Offshore Technology Conference This paper was prepared for presentation at the 2002 Offshore Technology Conference held in Whereas conventional solutions, such as hardware Houston, Texas U.S.A., 6–9 May 2002. multiphase metering, supply a limited information, our This paper was selected for presentation by the OTC Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as methodology works as an overall field supervisor for: presented, have not been reviewed by the Offshore Technology Conference and are subject to • estimating individual well production with respect to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Offshore Technology Conference or its officers. Electronic reproduction, appropriate pressure and temperature measurements; distribution, or storage of any part of this paper for commercial purposes without the written consent of the Offshore Technology Conference is prohibited. Permission to reproduce in print • detecting abnormal behavior (sensor drift for is restricted to an abstract of not more than 300 words; illustrations may not be copied. The instance); abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. • validating hardware measurements, and replacing them in case of failure. Typically, in deep offshore Abstract production, hardware sensors are not replaced in case As production in very deep waters becomes a crucial of failure for financial and feasibility reasons. challenge for many oil companies, a better management of the This methodology is based on data reconciliation. It production is constantly required. This paper presents two assumes that measurements are not necessarily correct and can complementary methodologies for operation support and be corrected within a confidence interval. Meanwhile, improvement of the production conditions. The first one is unmeasured variables derive from redundancy between flow based on data reconciliation between process measurements modelling and field data. and flow modelling. It brings an additional level of Data reconciliation has been already successfully applied information to the problem of continuous metering of deep- to a small production network, see Ref. 1. Our paper intends to water subsea wells. As periodic well testing is required to go further in the study of this innovative technology and achieve this predictive metering, the second methodology presents its application to the Girassol field. provides the optimal test sequences of well permutations. It involves flow process simulation and algorithmical sorting, Well monitoring according to production constraints and operating strategies. Given a set of temperature and pressure measurements, our Finally, comparisons between numerical simulations and plant methodology aims to provide an estimate of the phase flow data demonstrate the ability of these two methodologies to rates produced by each individual well of an oil field. provide strong and reliable information for deep offshore producers. Problem modelling. Real-time plant data are completed with a global steady state simulation of the production network Introduction involving: The knowledge of the phase flow rates coming from each • mass, force, and heat balance equations; individual well of an oil field is mandatory for a better • thermodynamic calculations; production and reservoir management. Generally, this • hydrodynamic modelling. information comes from a series of direct well testing, where a For instance, assuming process data at both ends of a single well flows directly to a test separator. choke and an estimate of the fluid composition, one can derive In deep offshore, this procedure turns out to be a local estimate of the liquid and gas mass flow rates from inappropriate: production developments are based on hydrodynamic and thermodynamic calculations. gathering network, where manifolds merge the production Combination between physical modelling and plant data is from several wells into a single flowline. This is the case on applied to the whole production network, leading to multiple the Girassol oil field in Angola, see Fig. 1. Moreover, direct estimates of the same information. This multiplicity derives well testing implies deferred production, valve reliability and from our uncertain interpretation of the reality: this statement flow assurance issues: hydrate formation in dead branches, is precisely the main strength of the data reconciliation slugging at low flow rates, etc. technology.
  • 2 E. ZAKARIAN, A. CONSTANT, L. THOMAS, M. GAINVILLE, P. DUCHET-SUCHAUX, AND P. GRENIER OTC 14009 Whatever the complexity of a model, one should be aware (calibrated) might be trusted with a relative confidence as the that it always remains an approximation. However, the latter fluid composition may change between two consecutive can be restricted to a small number of modelling parameters measurements performed on a test separator. Therefore, embedded in residual equations. Again, any hardware sensor equations (1) to (4) are replaced by residual equations and contribution is a residual equation weighted by vendor weighted by the standard deviation (uncertainty): uncertainty. ePi = (Pi - Pmi)/σPi, .......................................................... (9) Both hardware measurements and modelling equations are involved on the same level of analysis through a global data eTi = (Ti - Tmi)/σTi,......................................................... (10) reconciliation and parameter estimate, leading to an optimization problem. Conversely, experience feedback is eBSW = (BSW - BSWm)/σBSW, ......................................... (11) expected to get optimal values of model uncertainties. A major innovative aspect of this work is the systematic eGOR = (GOR - GORm)/σGOR,........................................ (12) computation of the accuracy of any estimated variable. We notice that the accuracy of a measured variable can be slightly where i = 1, 2, and 3. increased since data reconciliation works as a global As reminded before, an additional parameter calibrates computation where any piece of information is likely to be each modelling equation with respect to a reference value improved by the other ones. (tuned from test measurements) and an uncertainty. Four We also emphasize that information on the solution residuals complete the system: uncertainty is as important as the solution itself: whenever eWP = (ΓWP - ΓWP,ref)/σWP, ............................................. (13) redundancy remains, a physically incorrect solution can satisfy the problem. Therefore, a supervisor checks the consistency of eWT = (ΓWT - ΓWT,ref)/σWT, .............................................. (14) the solution against intuitive expectations. eCP = (ΓCP - ΓCP,ref)/σCP, ............................................... (15) Example. Let us consider the following scenario. Given one well tubing followed by a choke, six sensors are installed eCT = (ΓCT - ΓCT,ref)/σCT,................................................ (16) upstream and downstream these equipment to measure the We finally get 16 equations, namely (5) to (16), and 13 pressure and the temperature of the fluid, see Fig. 2. We derive variables: BSW, GOR, Flow, ΓWP, ΓWT, ΓCP, ΓCT, Pi, and Ti, six equations: where i = 1, 2, and 3. The system seems redundant. However, Pi - Pmi = 0,.....................................................................(1) redundancy can only be ensured from a detailed analysis: to avoid any singularity, the rank of the jacobian must be equal to Ti - Tmi = 0, .....................................................................(2) the number of the variables at any operating point. Note: the where i = 1, 2, and 3 refer respectively to the following jacobian of the system is the matrix given by the partial positions: upstream the well tubing, downstream the well derivatives of the equations (modelling equations and tubing, and downstream the choke. residuals) with respect to the variables. Calibrated values of the water-liquid ratio and gas-oil ratio This condition is necessary to find a solution, which is a give two additional equations: minimization of an objective function given as a sum of the squares of the residuals. Meanwhile, the modelling equations BSW - BSWm = 0,............................................................(3) are exactly satisfied (optimization constraints). GOR - GORm = 0............................................................(4) Three phase flow modelling. Several models are involved in A well tubing model provides a pressure drop relation and the simulation of a Girassol production loop, see Table 1. As a a heat balance between positions 1 and 2: mixture of oil, gas and water is expected during the field life, fWP (P1, T1, P2, T2, Flow, BSW, GOR, ΓWP) = 0,..............(5) intensive efforts are required to get an acceptable physical representation, due to the complexity of three-phase flows. fWT (P1, T1, P2, T2, Flow, BSW, GOR, ΓWT) = 0. ..............(6) A gridded modelling is used for long tubings (well, sea- line, riser) since local effects such as slope changes may A choke model gives the same kind of relations between strongly impact pressure and thermal profiles. A complex positions 2 and 3: three-phase hydraulic module ensures a correct representation fCP (P2, T2, P3, T3, Flow, BSW, GOR, ΓCP) = 0,...............(7) of the different flow regimes. Local flash and thermal calculations improve the physical modelling as well. fCT (P2, T2, P3, T3, Flow, BSW, GOR, ΓCT) = 0................(8) A rather sophisticated modelling is used for the chokes since flow criticity and gas expansion may seriously affect Every measurements involved in this system are not pressure and temperature variations. The choke discharge necessarily correct since hardware sensors are subject to errors coefficient must be initially calibrated and periodically and the flow is not perfectly stable in the entire production validated against field data. Three-phase flow meters could be system. As far as BSW and GOR, their a priori values used. Meanwhile, test separator instruments provide sufficient
  • OTC 14009 DEEP OFFSHORE WELL METERING AND PERMUTATION TESTING 3 and accurate information for calibration. executive framework. The latter runs the whole application This tuning procedure is described in a further section. In and its components, like the configuration of a production terms of computation, tuning is a particular use case of this network or the use cases of a well monitoring application. well metering methodology. Sensor failure detection. The data interface component Thermodynamic issues. Physical phenomena such as makes the connection between the simulator executive vaporization in wells or expansion in chokes require accurate framework and external components that provide hardware thermodynamic calculations. Therefore, the mixture measurements: Distributed Control System, database. composition is tracked along the flow line, and the whole unit To avoid any undesirable effect on data reconciliation, the operations perform local vapor/liquid/liquid equilibrium data interface performs a preliminary analysis to detect any calculations to get an estimate of the phase properties. possible failure (unlikely value, excessive variation) or However, it is an illusion to believe that an accurate estimate unsteady behavior when the average of the measurements of the reservoir fluid molar composition can be found: neither depends significantly on time. the system, nor the available sensors are able to catch the A sensor is declared invalid in case of failure and its effect of a composition change (at constant phase properties) contribution is removed from the system. It does not between C9 and C10 cuts, for instance. Conversely, gas participate to the data reconciliation, leaving the determination coning or water breakthrough does affect sensor data. of the measurement to the optimizer. This preliminary Therefore, the fluid composition is corrected with BSW and detection is as important as the data reconciliation itself. Let GOR variations by addition or removal of water and/or gas us present an example to confirm this statement. from the first stage separator. We consider a network with a well producing in a single flowline through a choke and a manifold, see Fig. 4. Pressure Software issues. The basic domains involved in an industrial and temperature sensors are located upstream and downstream well monitoring tool are: these equipment. Assuming an initial calibration of the system, • compositional thermodynamic calculation; we define a particular scenario with hardware sensor failures, • hydrodynamic modelling in pipes and wells; see Fig. 5: at a fixed period of time (five minutes), the data • thermal modelling in pipes and wells; interface sends sensor measurements to the simulator • valve modelling; executive framework and a new solution is computed. Forty- • reservoir PI modelling; five minutes after starting up, the pressure sensor at the bottom • data reconciliation; hole returns zero, which is of course an unacceptable value. • real-time process data recording and analysis. This failure lasts half an hour. Two hours after starting up, both pressure sensors at the bottom hole and the manifold For each domain, several approaches have to be tested and return zero again. selected. For example, a particular thermodynamic server might be suitable for certain operating conditions but First, let us consider the case where the data interface does not perform a detection of sensor failure. At the beginning of unacceptable to other cases. Note: a server is a software the run, the simulator computes reconciliated data, see Fig. 6. component that provides services for other software components (client components) through defined interfaces. The latter are very close to the real measurements reproduced on the figure as dotted lines. Therefore, it should be easy to combine different modules At 45 minutes where the first pressure sensor collapses, the with the minimum effort. In addition, many pieces of software from different sources might fulfill our requirements. For simulator manages to rebuild a measurement at the bottom hole. The rebuilt value is physically acceptable but likely far these reasons, we decided to build our well monitoring from reality. At the same time, the production is overestimated simulator as an open software, using the CAPE-OPEN (the productivity index remains the same in the whole run) and (Computer-Aided Process Engineering) standard, see Ref. 2. the gas-oil ratio decreases, see Fig. 7. The wellhead pressure The compliance to this standard is another major and is also strongly affected because of its direct dependence on innovative aspect of this work. It provides high model the bottom hole pressure through the well tubing model. The flexibility for the end user, it makes implementation of new pressure drop increases by 106 Pa. features much easier and faster: plug and play integration of any CAPE-OPEN compliant component is carried out with the At 75 minutes where the sensor starts running again, the initial solution is recovered but at 120 minutes, both pressure minimum effort. sensors at the tubing ends stop to run. The simulator fails to Built on a component-based architecture, see Fig. 3, our well monitoring simulator includes: find a solution. Let us run the same simulation but with detection of sensor • CAPE-OPEN compliant components: unit operations, failure. We notice that the data reconciliation works perfectly thermodynamic servers; in this case, showing its ability to rebuild unmeasured • external components: solver, man machine interface, variables, see Fig. 8, 9. The sensor failure does affect the data interface, supervisor. redundancy of the problem, which is lower than before, but it CAPE-OPEN standard interfaces ensure the does not affect the results. communication between components through a simulator
  • 4 E. ZAKARIAN, A. CONSTANT, L. THOMAS, M. GAINVILLE, P. DUCHET-SUCHAUX, AND P. GRENIER OTC 14009 At 120 minutes, both sensor failures are detected at the We calibrate the simulator with data recorded on well tubing ends. The solution remains acceptable. However, December 7th, between 02:00 and 08:00 am. Then, we run a the manifold pressure drops significantly and its a posteriori metering, up to December 12th. confidence interval as well. In other words, there is not enough With a sampling DCS period of five minutes, production is redundancy to trust the reconciliated value of the manifold estimated every thirty minutes, using data filtered on the past pressure. hour. If we assign the same level of confidence to the models This second computation confirms the ability of our (choke, inflow, tubings), the system overestimates the methodology to replace hardware sensors in a production production, but the predicted trend is consistent with reality, system. It also shows its weakness whenever the number of see Fig. 12. valid sensors is not sufficient to allow redundancy. A detailed analysis shows that the choke model is responsible for this deviation: tuning with subsequent tests Real-scale validation shows that the discharge coefficient drops from 0.92 to 0.6 on Production at the Girassol field started in late 2001. We December 8th, see Fig. 13. Meanwhile, productivity index and propose to use a first series of measurements to validate our friction factors remain roughly constant. This observation methodology and confirm its ability to provide reliable reveals some inconsistency between plant data and the choke information. These measurements were recorded from modelling. Further analysis will be required to get a better December 4th to 24th, 2001. understanding of the real choke behavior. If we set a lower relative confidence on the choke model Calibration. We focus our presentation on a single well (by increasing the discharge coefficient uncertainty), we verify flowing into the right branch of the P10 loop. We consider the that the initial tuning is sufficient to get, five days latter, a network from the well tubing to the test separator on FPSO good estimate of the expected oil and gas production, see Fig. (Floating Production Storage and Offloading), see Fig. 10. 14. This implies that the initial tuning of the GOR was good Note that riser tubing and riser choke are not simulated in this enough for the five following days of metering. We effectively presentation. notice that the GOR derived from tuning remains constant, see Modelling parameters derive from a single simulation with Fig. 15. the following configuration: • At the test separator: set the uncertainties of the phase Permutation testing Assistance tool flow-rate sensors to vendor accuracy (we assume Periodic calibration is required to keep the modelling close to accurate measurements) the real process. Three-phase flow meters could be used but • At the upstream equipment and inflow model: remove test separator measurements through well testing can also the residuals of the modelling parameters (GOR, BSW, provide sufficient and accurate information. choke discharge coefficient, productivity index and Production at the Girassol field is based on a loop friction factors). configuration where sea-lines are connected to each other The vendor accuracy of the flow-rate sensors is small. through subsea manifolds, see Fig. 1. Each well of a loop is Therefore, the phase flow-rates computed by the optimizer are routed to a production line, either left or right. There is no necessarily close to their measured values. Meanwhile, the specific line for well testing. phase flow-rates derived from the modelling have to match This network architecture and flow assurance issues these measured values. Assuming a small uncertainty on the strongly impact the well testing strategy: pressure/temperature measurements, the optimizer is forced to • direct testing leads to increase deferred production; change the values of the modelling parameters (a particular • direct testing at low flow-rates may lead to instability situation occurs here since the system has no redundancy, and in the flowlines (slugging); solution is independent on the sensor accuracy). • direct testing at flow rates below 10 000 bbl/d may Twice a day in December 2001, measurements on a test lead to a fluid temperature lower than the paraffin separator were carried out at the Girassol field to estimate the formation temperature (about 40°C); production of the well. We propose to calibrate our system on • direct testing for the nearest wells leaves the upstream one of these tests. Then, after a certain period of time, we will flowline full of dead fluid during the test, and hydrate compare the oil, water, and gas flow-rates predicted by the inhibition with methanol is required. simulation and those derived from real testing. Therefore, permutation testing (in addition to direct testing) has been included in the Girassol well monitoring Well metering. A demonstrative test can be found from strategy: December 7th to 12th. During this period of time, the • a direct testing connects a single well to a single production of a well was progressively increased with a choke production line; estimating the phase flow rates of the opening from 36 % to 49 %, see Fig. 11. Meanwhile, the well is straightforward; pressure drop in the well tubing remained approximately • a permutation testing connects several wells to both constant and the one through the choke dropped from 5 106 Pa production lines (but a well is necessarily allocated to to 2.5 106 Pa. a single line). A series of different permutations leads
  • OTC 14009 DEEP OFFSHORE WELL METERING AND PERMUTATION TESTING 5 to different measurements of the phase flow rates of the maximal accuracy. For instance, if we consider a each production line. The phase flow rates of each production loop with four wells, sixteen different sequences of individual well derives from solving a set of linear four direct tests will estimate the production, without any loss equations. of accuracy. But, only few of them may satisfy operation constraints. Testing strategy. Since the wells of a production loop may According to our assistance tool, only one sequence does produce into either left or right production line, a permutation not require hydrate inhibition with methanol, see Fig. 17: no can be considered as a set of two well arrangements, left and dead branch is created if we consider the first arrangement as right. The number of possible arrangements is necessarily the initial loop configuration. Conversely, if we accept a much greater than the number of wells. For example, let us relative loss of accuracy, permutation testing will keep the consider four producers: WA, WB, WC, WD. Any sequence production at its optimal level, see Fig. 18. involving these four wells can be acceptable. One of them is Since both maximal accuracy and minimal production loss shown on Table 2; notation: WA + WB + WD means a test with strategies may be required, a global weighted criterion is WA, WB, and WD. actually used to bring all the strategies together and perform The number of possible test sequences increases drastically the sequence sorting. with the number of wells, see Table 3. This observation and Theoretically, there is no limitation on the number of wells the complexity of involved phenomena prevent us from to consider. However, let us remind that the number of deriving a simple synthetic rule that could be used in operation possible test sequences increases drastically with the number to select the best test sequences. The latter have to be of wells, see Table 3. In the case of six wells or more, the compliant with the whole production constraints. computation time can be prohibitive unless one or several A permutation testing assistance tool has been specifically wells are exclusively allocated to a production line. designed to achieve this work. Basically, a steady state process simulator is used for network and gas–lift computation, Conclusion providing well production in any arrangement, see Table 4 for This paper demonstrates the ability of a well monitoring few examples. Then, sequence sorting is carried out versus software to provide reliable information for producers: user strategy, see Fig. 16. production estimate of each individual well, abnormal behavior detection, validation of hardware measurements and Flow modelling. Flow rates and production losses are replacement in case of failure. estimated from a simplified flow modelling: well performance Although the described methodology can be applied to any curves and pressure loss tables derive from experiments or type of onshore/offshore development scheme, this work is simulations performed on predictive multiphase software. mainly intended to deep offshore developments, such as the Specific unit operations implement these tables in the process Girassol field in Angola. simulator. Thus, any code can be used to generate the flow Based on data reconciliation between field data and flow modelling. modelling, our well monitoring tool requires a periodic Contrary to the well monitoring tool, this permutation calibration to keep its modelling close to the real process. This testing assistant is an off-line software. However, a periodic tuning derives from test separator measurements. tuning against real process data is recommended in order to Since a combination of direct and permutation well testing adjust BSW or GOR of each individual well. is presently involved at the Girassol field, we also designed a second tool to compute the optimal test sequences versus usual Sequence sorting. After network configuration and production and operating constraints. calculations, a sorting service provides an ordered succession Intensive use and positive feedback will confirm the of permutations. usefulness and reliability of this work. This will be the main The initial number of possible sequences is Ckj where k is topic of a second paper. the length of the sequence and j is the total number of possible arrangements, see Table 3. Some of them do not comply with Nomenclature thermal constraints and are removed. The same applies for BSW = Basic Sediment and Water (water volume singular sequences. flow/liquid volume flow), m3/m3 For one sequence of k arrangements, there are k! orders. BSWm = Measured value of BSW, m3/m3 Applying an order-dependent criterion, the best order is Flow = Total mass flow rate, kg.s-1 selected. The simulator computes the expected test accuracy, GOR = Gas-Oil Ratio (gas volume flow/oil volume namely the accuracy of each individual well production, GOR, flow), Sm3/m3 and BSW. Finally, the remaining sequences are sorted with GORm = Measured value of GOR, Sm3/m3 respect to user strategy, see Fig. 16. Typical simulation results P1 = Well tubing upstream pressure, Pa are shown on Table 5. P2 = Well tubing downstream pressure, Pa P3 = Choke downstream pressure, Pa Features. Direct testing is intuitively the best solution to reach Pmi = Measured value of Pi, (i = 1, 2, 3), Pa
  • 6 E. ZAKARIAN, A. CONSTANT, L. THOMAS, M. GAINVILLE, P. DUCHET-SUCHAUX, AND P. GRENIER OTC 14009 T1 = Well tubing upstream temperature, K T2 = Well tubing downstream temperature, K Choke Pm3, Tm3 T3 = Choke downstream temperature, K Tmi = Measured value of Ti, (i = 1, 2, 3), K Pm2, Tm2 ΓWP = Tuning parameter for well tubing pressure drop ΓWT = Tuning parameter for well tubing heat balance ΓCP = Tuning parameter for choke pressure drop ΓCT = Tuning parameter for choke heat balance Well tubing ΓWP,ref = Calibrated value of ΓWP ΓWT,ref = Calibrated value of ΓWT ΓCP,ref = Calibrated value of ΓCP ΓCT,ref = Calibrated value of ΓCT σPi = Uncertainty of Pmi (i = 1, 2, 3) Pm1, Tm1 σTi = Uncertainty of Tmi (i = 1, 2, 3) Fig. 2: Example of a production network σBSW = Uncertainty of BSWm σGOR = Uncertainty of GORm EXTERNAL σWP = Uncertainty of ΓWP COMPONENTS σWT = Uncertainty of ΓWT Man Machine Interface Supervisor σCP = Uncertainty of ΓCP σCT = Uncertainty of ΓCT Solver DCS References 1. Van der Geest, R., “Reliability Through Data Reconciliation”, OTC 13000 presented at the 2001 Offshore Technology CAPE-OPEN Conference held in Houston, Texas (2001). Simulator Executive SIMULATION 2. Braunschweig, B., Paen, D., Roux, P., and Vacher, P., “The Use of Framework ENVIRONMENT CAPE-OPEN Interfaces for Interoperability of Unit Operations and Thermodynamic Packages in Process Modelling”, The European Refining Technology Conference, Paris, France (2001). See also http://www.colan.org. Unit Operation Thermo Server Figures CAPE-OPEN COMPONENTS Wellhead Manifold Fig. 3: Well monitoring software overview Fig. 1: Girassol subsea loop Fig. 4: Sensor failure simulation
  • OTC 14009 DEEP OFFSHORE WELL METERING AND PERMUTATION TESTING 7 3.00E+07 3.00E+07 2.50E+07 2.50E+07 2.00E+07 Pressure (Pa) 2.00E+07 1.50E+07 Reconciliated bottom hole pressure Reconciliated wellhead pressure Pressure (Pa) Reconciliated manifold pressure 1.50E+07 1.00E+07 Bottom hole pressure Wellhead pressure 1.00E+07 M anifold pressure 5.00E+06 0.00E+00 5.00E+06 0 20 40 60 80 100 120 140 160 Time (min) Fig. 8: Reconciliated pressure sensor measurements (activated 0.00E+00 failure detection) 0 20 40 60 80 100 120 140 160 Time (min) 1.60E+02 Fig. 5: Pressure sensor measurements 1.40E+02 3.00E+07 1.20E+02 2.50E+07 1.00E+02 2.00E+07 Gas-Oil Ratio (Sm3/m3) 8.00E+01 Pressure (Pa) Total mass flow rate (kg/s) 1.50E+07 6.00E+01 Reconciliated bottom hole pressure Reconciliated wellhead pressure Reconciliated manifold pressure 1.00E+07 4.00E+01 2.00E+01 5.00E+06 0.00E+00 0.00E+00 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 Time (min) Time (min) Fig. 9: Production estimate (activated failure detection) Fig. 6: Reconciliated pressure sensor measurements (no preliminary failure detection) 1.60E+02 1.40E+02 1.20E+02 1.00E+02 8.00E+01 Gas-Oil Ratio (Sm3/m3) Total mass flow rate (kg/s) 6.00E+01 4.00E+01 2.00E+01 0.00E+00 0 20 40 60 80 100 120 140 160 Time (min) Fig. 7: Production estimate (no preliminary failure detection) Fig. 10: Typical Girassol production line
  • 8 E. ZAKARIAN, A. CONSTANT, L. THOMAS, M. GAINVILLE, P. DUCHET-SUCHAUX, AND P. GRENIER OTC 14009 Bottom hole pressure Wellhead pressure Choke downstream pressure Choke opening Time Fig. 11: Pressure measurements and choke opening (%) at a Fig. 14: Girassol well: production simulation (second case) Girassol well Fig. 15: Girassol well: GOR calibration Fig. 12: Girassol well: production simulation (first case) Production loss Oil production uncertainty Subsea valve Water production operation uncertainty Dead branch Gas production creation uncertainty Methanol consumption Fig. 16: Various strategies for well permutation sequence sorting Fig. 13: Girassol well: calibration of the modelling parameters
  • OTC 14009 DEEP OFFSHORE WELL METERING AND PERMUTATION TESTING 9 P1021 P1011 Fluid source Gas-lift model P1022 Right Manifold Connection between wells and production loop Three-phase flow model. Gridded model. M102 M101 Pipeline TEST Used for well tubing, sea-line, and riser Left Piping Simulation of small scale piping networks P1012 Sensor Hardware sensor model Definition of fluid composition and Productivity Inflow P1021 Index relation P1022 P1012 Table 1: Well monitoring unit operations Right M102 M101 Test number Well arrangement Left TEST 1 WC 2 WA + W B + WD P1011 3 WB + WC 4 WB + WD P1011 P1021 P1012 Table 2: Example of a well test sequence Right Number of Number of well Number of well testing M102 M101 wells arrangements sequences Left TEST 3 12 220 4 28 20475 P1022 5 60 2.12E+06 P1011 6 124 1.52E+09 P1012 P1022 Table 3: maximal number of well testing sequences Right M102 M101 TEST Left P1021 Fig. 17: direct testing sequence P1021 P1022 Right M102 M101 TEST Left P1012 P1011 P1021 P1012 Right M102 M101 TEST Left P1022 P1012 Table 4: Well permutation tests P1021 P1012 Right M102 M101 TEST Left P1022 P1011 P1021 P1012 TEST Right M102 M101 Left P1022 P1011 Fig. 18: Permutation testing sequence Tables Unit operation Description Block valve Routing valve (open/closed) Choke Three-phase model Table 5: Test sequences (production loss minimization)