Emeraude v2.60 White Pages

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1 Emeraude v2.60 White Pages 1. Introduction Emeraude 2.60 contains major changes, as well as a series of smaller additions which together, play an important part in further enhancing the interaction with the software. As always, the goal with this version is to provide our users with a sound and comprehensive set of tools, through an intuitive interface allowing a clear understanding and complete control of those tools. The purpose of this document is to describe the principal changes and explain what they bring. The objective is not to describe the software interface but to give an overview of the new methods and workflows. If you are a v2.50 user, you can complement this reading with the Emeraude v2.60 Updates Notes, which give a detailed account of the additions/changes since the previous version. 2. A new interpretation method: the Continuous approach 2.1. What we have been doing since v1.0? Since version v1.0 in 1996, the methodology in Emeraude has been based on non-linear regression, as it offers a flexible framework permitting the inclusion of any tool, the addition of external constraints such as surface rates, sign constraints, etc. A regression in the general sense is the solution to an inverse problem, i.e. finding the input to a model so that the corresponding output is as close as possible to the available measurements. There are 2 distinct regression schemes in Emeraude: (1) the Local improve, that worries only about a particular depth -or zone- seeking the rates Qw,Qo,Qg on that zone, and (2) the Global improve, which solves for the contributions of all the inflow zones at once [dq w i ] i, [dq o i ] i, [dq g i ] i The Local Improve, illustrated below, relies on the definition of some Direct model comprising an assumption of slippage, frictions, with whatever else is needed for the particular situation, and seeks the rates such that corresponding simulated values match a set of measurements sufficient to determine the problem. An important point to note is that in this case, the holdups are determined exactly from the slippage correlation and only the rates are treated as unknowns.

2 In the Global Improve, the same direct model as in the Local Improve is used. For every iteration, the assumption of the dq s translates into a series on Q s on the calculation zones, which can be injected into the direct model and the objective function calculated. Here again the objective function is evaluated on the calculation zones only; and to this error, other components might be added such as a constraint using the surface rates. Whether the regression is local or global it is only influenced by the solution on the few user defined calculation zones, and for this reason we refer to this approach as the Zoned approach The Continuous method: match everywhere with loose slip model compliance The clear advantage of the Zoned approach is speed, since only a few points are required to get an answer. Its main drawback is that the results are driven by the choice of calculation zones. A way to remove this dependency is to run a Global Improve with the errors evaluated everywhere on the logs, and not only at a few points. We could for instance seek to minimize the difference between the data and the schematic logs everywhere. When we look at match views however, we see that the schematics are fairly square in shape; this is because between inflow zones the mass rate does not change, and since we honor a slip model, there are little variations in holdups and deduced properties. The only way to account for the changes seen on the data is to let the holdups differ from the model prediction. And this leads to the final formulation of a new Global Improve described below. The main regression loop is still on the contributions but the objective function considers an error on the log points. In turn, at each depth, the simulated log values are evaluated by running a second regression on the holdups to minimize an error made of the difference between simulated and measured values, and at the same time, a new constraint using the slip model holdup predictions. In other words the holdups are freed, but the process tries not to go too far from the slip model prediction. In cases where one can do without the slip model, this new constraint is obviously not included.

3 2.3. Illustration with a 3-Phase example The Fig. below shows the result of the Zoned approach on a 3-phase example (Emeraude Guided session 3). The calculation zones were defined see how the second perfo has been split; a Local improve has been run, followed by a Global improve with the constraint that all zones produce. This example is toggled to Continuous and a Global improve rerun. In the end, there are no real differences. The match looks better overall, but at the expense of not honoring the slip models. The deviation from the slip models is indicated in the rightmost track (line=model, markers=solution). Note that the number of depth samples on the logs is user defined.

4 2.4. So? In the above example there is no clear benefit in using the Continuous method. But there are cases where the converse will apply, for instance when the data is unstable between contribution intervals the location of the calculation zones may dangerously impact the Zoned method results. Note however, that the continuous method is also influenced by the location of the calculation zones inside the inflows since the split determines the number of unknowns and has a direct influence on the shape of the simulated logs over the inflows. Another situation where the Continuous method might provide a better answer is with temperature, since the temperature varies continuously. In Emeraude v2.60 the two methods, Zoned and Continuous, are offered in parallel and you can switch from one to the other at any stage. You can start with Zoned, toggle to Continuous, and decide which one you like most It is important to stress that a nice looking match does not necessarily mean a right answer. It all comes down at some stage to the interpreter s judgment. Also, any regression is biased by the weights assigned to the various components of the objective function. Different weights will lead to different answers; starting point will also be critical as a complex objective function will admit local minima. So the Continuous Approach is not a magical answer, and more complex does not necessarily mean better. 3. Multiple Probe Tool (MPT) analysis revisited Working with multiple probe tools is not simple. Simply loading and visualizing the data can be a challenge in itself, due to the shear amount of curves. Sometimes, the data becomes understandable only after some processing has been made (e.g. SAT spinners in a rotating string). The objective of v2.60 was to restructure the MPT options in order to offer a fast a flexible workflow to load the data, build and store layouts efficiently, review the data quality in a number of presentations: logs, image views, crosssections, as well as offer an extensive framework for the quantitative interpretation Facilitating the display With the Emeraude installation a number of pre-defined templates are provided. The template option has been modified so that it becomes very easy to generate a series of image views for a group of passes, to apply the same binding to several templates in one go, etc. Snapshots can be made at any stage, so right after loading the raw data you can create snapshots for say, the raw spinners, the raw RAT readings, the FSI electrical probes, and so on. In fact, if you use a template to do this, the snapshot can be created automatically. MPT Templates With MPT tools, one needs to work with a very large number of views and sometimes is it hard to find the desired one. Filtering options have been added to the hidden view dialog to make this faster. Also, a cyclic display mode is available where you can define a visible range of more than 16 views and simply navigate through the cycle.

5 Show/hide view dialog 3.2. Image views/cross sections For circular tools, combined image views can be displayed. Below the first two views show passes D1 and D2, while the third one shows a combination of the two. A cross-section now also includes a plot indicating the projection of the readings in the vertical axis; for a combined view the points appear with the relevant pass color. Combined view (third) and corresponding cross-section

6 Image views can be seen from the top, the bottom, or the side. For velocity, the views directly use the Vapp channels produced by the calibration for each spinner/pass. As explained in section 3.8, the MPT processing in v2.60 generates reconstructed channels for the discrete measurements. So if one applies the process to passes D1 and D2, the interpretation will contain reconstructed curves for both passes for all discrete sensors. When an image view is built, one can choose to use the raw or the reconstructed values, which gives a very good indication of what the model has done. This is also exemplified in the cross-sections, see the FSI example below. For FSI, note that there is no combined mode equivalent on the view itself, but this mode is applicable on the cross-section MPT tool responses FSI images and cross-section (from reconstructed values) Sondex RAT When loading RATMN channels, they are treated as water holdups but they can be renormalized. Often the delivered data shows Water=0.5 and Hydrocarbon=1. The calibration values are provided for each of the 12 probes, and it is possible to force the normalized values to lie within the interval defined by the normalization. RAT mean normalization CAT.RGB

7 Sondex CAT A 3-phase calibration is defined in v2.60 from the sides (2-phase) responses of the rgb file (see above): 3-Phase CAT response - Note the 2-phase segments: Oil-Gas; Oil-Water; Gas-Water As for the RAT tool, when applying a normalization to CAT raw data, it is possible to force resulting values to lie within the interval defined by the normalization. FSI The FSI spinner calibration mode based on pro-rated slopes and thresholds has been removed. This is replaced by a Constant calibration mode with a slope/threshold couple per spinner. The Conventional insitu calibration mode remains available. The FSI spinner measurements can be strongly influenced by the tool body during the Up and Down passes. To account for this effect, the raw measurements are corrected a priori by multiplying them by the Up correction factor. However, for negative rps values, the Down correction factor should be used. Emeraude 2.60 offers this possibility MPT processing by regression The new MPT processing is materialized by an option in the Interpretation panel. Compared to previous versions where this process was included in the browser, the new structure offers the significant advantage that several interpretations using different MPT processing can co-exist within the same survey and comparisons can be made between them. When dealing with MPT data the hope is that the discrete values of holdups and velocity can be combined to define local phase velocities. By integrating this information over the cross-section at every depth, we can produce phase rates, hence waving the need for slippage models. The main challenge is to define adequate 2D representations of the holdups and the velocity. The general framework considers that we have a set of discrete values, and some model with which we want to match those values. We treat this situation as a standard regression problem where the model parameters are found by minimizing the error between the raw and the reconstructed values. Basically, if we write the list of discrete values and their coordinates and our model the function we seek to minimize is:

8 In v2.50, using Mapflo or Smooth spline actually led to doing just that behind the scene. The main advantage of using this general formulation is that we can then easily add external constraints. In v2.60 for instance, a physical constraint can be incorporated that expresses vertical segregation: water holdup decreases from bottom to top, and gas holdup increases from bottom to top. We can also easily add constraints using other tools. So for instance, if we are seeking the holdup profile and want to constrain the results by a density measurement, we can simply add a term in the above objective function comparing the density derived from the 2Dmodel and the measured one. Below is the list of 2D models implemented in v2.60. Linear This model uses as many internal variables as the number of valid (non N/A) distinct projections of discrete values on the vertical axis. The model assumes a vertical interpolation on the z axis between the internal values, and a horizontal extension of the central values. Mapflo Schlumberger only This model uses two internal parameters. The model defines the shape of the vertical profile. The central values are extended laterally. Prandtl FSI only This model represents the velocity profile by a deformation of the holdup profiles, followed by a deformation near the edge Model application Schlumberger tools Electrical probes: for a PFCS or an FSI the discrete measurements represent Yw. They are matched independently with a 2D Model that defines Yw everywhere in the cross-section. Optical probes: GHOST, or FSI; same as above for Yg. FSI spinners: the Vapparent for each spinner is calculated beforehand. An independent 2Dmodel matches the local Vapps. Sondex tools CAT: The CAT does not relate directly to the holdup of a particular phase except in 2-phase. When matching the CAT independently, the user is thus requested to make a hypothesis. Possible hypotheses are that one phase is absent, or that the probe is in 2-phase Water-Oil or Oil-Gas mixtures; the latter is similar to the assumption made implicitly with rgb files. RAT: The RAT readings relate directly to Yw. They can be matched independently to give Yw. RAT+CAT: A single 2Dmodel with internal variables as Yw and Yg can be used to match simultaneously the two measurements. This relies on the availability of a 3-phase CAT response. SAT spinner: as with the FSI the Vapparent have been calculated before; we match the probes independently Additional processing options User constraints The following constraints can be imposed where relevant: One phase absent W-O/O-G: used for the CAT to consider either Water-Oil or Oil-Gas Gravity segregation (this can also be applied to the Mapflo model) Sum of holdups bound to 1. This is for the cases where Yw and Yg for instance are treated separately. Multiple pass application In v2.50 it was possible to repeat the processing on multiple passes. This option is supported in v2.60 but augmented with the ability to treat multiple passes at once. In this combined pass mode, the readings of all the selected passes are matched simultaneously at any given depth. In case of faulty probes with a rotating tool and a stable well...- this can help get a good answer. Bubble flow rate calculation for Schlumberger tools This option is based on a processing developed by Schlumberger to derive the oil / gas rate from the electrical / optical bubble counts. The calculation uses the average holdup and the bubble counts.

9 3.7. MPT processing output and display Process output The main output is the average holdups, mixture velocity, phase rates. Their mnemo and position in the hierarchy will depend on the tool type and the process options. The possibility is given to automatically average them when passes are processed independently, and to automatically copy the result to the interpretation input. Automatic views are generated as a result of the process to display the output. Reconstructed and error channels For any measurement with mnemo XXXX that is matched, Emeraude outputs a reconstructed version with mnemo XXXX_K, as well as the error between them with mnemo XXXX_KERR. The reconstructed values are also generated for ignored or disabled probes. When Tool constraints are considered, the corresponding reconstructed channels are also output, e.g. VASPIN_K for a flowmeter constraint (mnemo SPIN). As explained previously, image views can be built from the reconstructed channels and compared to the ones for the raw channels. A comparison between raw and reconstructed is readily available for the automatic views showing the raw data with a single click of a toolbar option. The next Figure illustrates how, on a layout comprising the 5 FSI apparent velocities, the raw data (solid) is compared with the result of a processing using the Prandtl model (dash). Global errors / constraints A cumulative error is stored for any particular tool or constraint. Interpretation views are built automatically to display them.

10 Emeraude v2.60 White Pages 10/12 4. New temperature model: Energy equation In the context of Rubis, the History matching module of Ecrin, we have worked for several years on the development of a multiphase, transient, thermal simulator, solving the temperature and pressure diffusions in the coupled wellbore-reservoir system. As part of this project, a simplified steady state version has been implemented that still incorporates a coupled T-P simulation inside the reservoir. The response of the simplified model has been validated using the full-fledged simulator for a number of situations. This new model, called the Energy equation model is now included in Emeraude in addition to the former, simpler Segmented model that couples an enthalpy balance in front of the inflows, with a Ramey conduction/convection equation outside. The energy equation model offers a number of advantages: No more exclusive segmentation with either conduction or enthalpy balance: everything is treated simultaneously. The formulation integrates the thermal compressibility effects in the wellbore and the reservoir. Joule-Thomson effects are automatically included without relying on some user defined pressure drop. The thermal effects within the reservoir are the result of the coupled influence of the compressibility effects and the conduction. Single phase gas example; Input from Rubis simulation matched with the Energy equation model The thermal exchanges between the wellbore and the reservoir are described in all models with the Heat loss coefficient (HLC), which can be set constant over the entire interval, or can be varied from depth to depth. The HLC can be obtained by a definition of the individual completion elements and properties.

11 Emeraude v2.60 White Pages 11/12 5. Leak option A leak option is now integrated, allowing the simulation of the thermal response due to a leak from the tubing into the annular. Emeraude starts from the wellhead conditions (imposed pressure, temperature and rate) and iterates until the pressure and temperature stabilize at max depth, fulfilling the mass and energy conservation equations. The calculation takes into account the heat transfer in the tubing and the annular by convection, between the tubing and the annular by conduction (and convection at the leak level), and between the annular and the reservoir by conduction. The thermal conduction along the tubing and the annular is also accounted for: the vertical thermal conduction may be a dominant phenomenon when very small rates occur in a vertical well. The temperature in the tubing and in the annular are calculated. 6. Steam injection option Steam injection can be of great help to enhance oil recovery when considering high viscosity oils: the heat transfer from the vapor phase to the oil phase, in the reservoir, allows lowering the oil viscosity and therefore facilitates its displacement towards the well. It is then of primary importance to correctly quantify the quality of the injected fluid at the level of each inflow (fluid quality = mass of vapor / total mass of injected fluid). The thermodynamic surface for water is shown opposite, and a dedicated PVT model has been implemented (Chlen-SPE 20319): In the two phase region (e.g. water and vapor), one can see that a direct relationship exists between pressure and temperature: if P is known then T is known (and conversely). The unknown is then the fluid quality. In the single phase region (e.g. water only or vapor only), the relationship between pressure and temperature is not unique, but the quality of the fluid remains known and constant (0 or 1). The problem is solved using the Energy equation model: the enthalpy balance is used to calculate the fluid quality when in the two phase region, and the temperature in the single phase region. Phase changes are correctly modeled thanks to the enthalpy balance. The heat loss to the annular and the formation can contribute to the condensation or the cooling of the injected fluid. Also, it can be noticed that the heat radiation and natural convection in the annular space makes the Heat Loss Coefficient temperature dependent, which adds non-linearity to the model. Hence, one cannot ensure, for instance, that pressure will increase as temperature increases down the well, while undergoing two-phase flow. Emeraude calculations proceed as follows. Starting from the conditions at a given depth (P, T, Qg, Qw), the flow correlation gives access to the pressure and the phase holdups at a depth below. Then, the enthalpy balance equation gives the temperature or the fluid quality, depending on the phase diagram. If the (P,T) conditions are such that two-phases are in equilibrium, the temperature is obtained from the PVT. The main results are the pressure and temperature profiles, and the water and vapor rate profiles. The Rate Calculation tab of the Zone Rate dialog gives the injected fluid quality. The flow in the well is modeled by considering the Liquid-Gas flow model. The possible choice of correlations has been limited to Petalas and Aziz, Stanford Drift Flux and Constant slippage. However, third party correlations are still possible, if one has developed his own specific correlation in the framework of the Emeraude External Flow Correlations.

12 Emeraude v2.60 White Pages 12/12 7. APERM This option is based on SPE , Permeability from production Logs Method and Applications by M.J. Sullivan et al and its objective is to correct an effective reservoir permeability curve, using the result of the PL interpretation. Starting from an existing PL interpretation, the Darcy s law is applied to each inflow to determine an effective permeability (k plt ). Considering the oil rate for a given inflow: k plt h = Q i B o µ o [Ln(r e /r w )-0.75+s] / (P avg -P wf ) where Q i is the oil contribution of the considered inflow (stb/d), µ o is the oil viscosity (Cp), B o is the oil formation volume factor, r e and r w are in consistent units, s is the skin, P avg is the layer average pressure and P wf is the well flowing pressure. If a liquid mixture is considered, the mixture rate is used and k plt corresponds to the oil effective permeability (or the water effective permeability if no oil is present). If gas is considered, pseudo-pressures are used and the formulation becomes: k plt h = e+6 Q i T [Ln(r e /r w )-0.75+s] / (m(p avg) -m(p wf )) where T is the inflow zone temperature. If a gas mixture is considered (e.g. condensate), the pseudopressures are calculated using modified gas gravity, weighted by the condensate. A permeability height product value (kh plt ) is then calculated by integration over the entire interval, and a normalized permeability is obtained for each inflow, by introducing a kh value determined by means of a well test analysis (kh bu ): k plt norm = k plt. (kh bu /kh plt ) A boost factor is then applied to the permeability measurement in order to match k plt norm at the level of each inflow. The matched permeability channel represents the reservoir effective permeability. The parameters required for each inflow zones are: The downhole rate, obtained from the interpretation QZI P avg, which can be calculated from a SIP P wf (and T) interpolated at the inflow top in the interpretation inputs µ calculated at P avg Re and skin=user entries Of course this method is only valid if a good grasp exists on the skin. [End of document]