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SPE 164420

Petrel Workflow for Adjusting Geomodel Properties for SimulationDavid R. Hoffman, Tatweer Petroleum

Copyright 2013, Society of Petroleum Engineers

This paper was prepared for presentation at the SPE Middle East Oil and Gas Show and Conference held in Manama, Bahrain, 1013 March 2013.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not beenreviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, itsofficers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission toreproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

During the initialization phase of a simulation modeling project, one of the most critical problems to overcome is theinevitable disconnect between the geomodel properties and reservoir performance in completion intervals. Thesedisconnects can be the result of incorrect perforations, simple data errors, and/or poor reservoir characteristics.Poor reservoir characteristics, in turn, can be the result of sparse or clustered well control, erroneous petrophysicalinterpretations, or improper model property distribution. A simple Petrel-based workflow is described which can beused to automatically modify geomodel properties in model cells associated with completion intervals. Using themodified model properties, the numerical simulation model can be initialized and the simulation engineer can workon other early-modeling issues, while the modified property can be used to isolate property modeling errors forfurther evaluation and correction in the geomodel.The workflow makes the basic assumption that if an interval has been perforated at any time in the well history,then that portion of the reservoir is assumed to meet minimum reservoir criteria for pay. Using the historical wellevents, a pseudo-log is created and used to generate a Petrel model property. This property is then used toevaluate and/or modify reservoir properties in completion zones. If a completed zone has reservoir propertieswhich do not meet pay cut-off criteria, that interval is modified by the workflow so that it passes the pay cut-offwithout changing other parts of the geomodel. In the Awali Field model tested, the modified cells constituted lessthan 1% of the total model volume and the modified geomodel eliminates non-connection errors in the simulationmodel (however, the reservoir quality issues still exist). Using property filters, the modified cells can then be usedto by the geomodeler to isolate the problem areas of the model for further evaluation and correction. Although thismethodology is presented using Petrel as the geomodeling platform, the same approach could be easily adaptedto most geocellular modeling applications.

Introduction

The use of 3D geocellular models has become both commonplace and necessary to conduct numerical simulationof reservoir performance. Although most geoscientists and engineers agree that the process of numerical

simulation is iterative, the feedback loop between reservoir engineers and geoscientists is often times less thanperfect. One of the most common problems experienced in the early stages of a simulation project is thedisconnection between actual production and perforated intervals in the geocellular model. Producing zones whichare not connected in the model can be caused by several problems (or a combination of problems), such as:

Incorrect Perforations Production Allocation Problems Poor Reservoir Characterization

Each of these problems must be evaluated and solved before an accurate numerical simulation will be meaningful.The first two issues can be solved through careful, though tedious, checking of completion and workover recordsand historical production data. The problem of poor reservoir characterization brings into question the fundamentalgeological and petrophysical assumptions which were used to generate the geomodel in the first place. These

problems are often the most time-consuming and difficult to resolve, as they can result from a lack of sufficient


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local well log control, problems with individual or local petrophysical interpretations, and/or incorrect statisticaldistribution of model properties.

Regardless of source of the problem(s), delaying the initial steps in the numerical simulation can be verydetrimental to the overall simulation project effort. Ideally, then, it would be very beneficial to have a method whichwould allow modification of geomodel properties, but only in zones associated with confirmed production zones.The workflow presented here provides that methodology, and results in a simulation-ready geomodel where

connection problems have been greatly reduced or eliminated altogether.

Assumptions and Limitations

The underlying and fundamental assumption employed in this workflow is that any completed interval in a wellwhich has been confirmed productive (including water-producing intervals) should meet minimum reservoir-qualityrequirements. While there are several geocellular modeling and data management applications available, theworkflow process presented in this paper assume the use of Schlumbergers Petrel modeling software andLandmark OpenWorks for data management.

Workflow

The workflow consists of six basic processes, not all of which will be required in all cases depending on the datasource available, or the stage in the overall geomodel construction:

Export of completion data from database Reformatting of data for Petrel Loading of production log data to Petrel Upscaling of production logs Creation of pseudo properties Modification of geomodel properties

Export of Completion Data Even if completion intervals are present in the geomodel, it is sometimes easier tostart with the raw data directly from the database. Using Landmark OpenWorks, raw completion data are exportedusing the Well Data Export utilities included in OpenWorks. Regardless of the source of the data, the goal is togenerate an ASCII-format text file which contains all the completions for each well in the field. The format of thisfile can vary, but in this example has a format as shown in Figure 1.

Figure 1 - Format of exported completion data

In this example, the output data includes the well name, UWI, measured depth of the top and base of theperforated interval, the type of well event (perforation, squeeze, etc.), the event data and event sequence, and theperforated interval or zone.


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Reformatting of Data for Petrel This workflow requires that the raw completion interval data be converted intopseudo-logs for each well, where completions are designated with values of 1 and the remainder of the well isdesignated with 0 values (i.e. binary curves). Because there is no commercial software which can easily reformatthe data, the author developed a simple Visual Basic application which is used to do the reformatting. Figure 2shows the user interface and summarizes the features and options of this program.

CUSTOM UTILITY TO RE-FORMAT COMPLETION DATAPROGRAM FEATURES:

Input & Output File Name Selection

Standard file selection dialogs

Output Filename & Extension

Allows removal of header lines

Preview of Input Data File

UNIX-DOS Conversion Option

Input File Format Options

Custom formatting of data columns

Figure 2 - Reformatting completion data for Petrel

Once the data have been reformatted using the utility, pseudo-logs will have been created for each well, althoughfor convenience all the logs are included in a single multiplexed file to facilitate loading to Petrel. Figure 3 showsthe reformatted data ready to be loaded to Petrel:


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After processing the input data file using

the custom utility, all completions areexported to a multiplexed set of data which

are in standard Petrel completion log

format. The log is a simple, binary

switch with 1 representing a perforation

and 0 indicating no perforation.

ORIGINAL INPUT DATA

COMPLETIONS CONVERTED TO

COMPLETION LOGS

Figure 3 - Data reformatted as production logs, and ready to be loaded to Petrel

Loading Production Logs to Petrel Now that the pseudo production logs have been generated they are loadedto the Petrel project database. It is assumed that the reader is familiar with the basic operation of Petrel withregards to data loading, but Figure 4 illustrates the basic steps in Petrel which are used to load production logs


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IMPORTING COMPLETION LOGS TO PETREL

The completion logs are importedby selecting the Import (on

selection) option from the context

menu on the Wells folder, then

selecting the Multiple well logs

option from the import dialog.

On the Input Data tab of the import dialog, set the column and file

format to match the input file format. If necessary, Units or Other

settings are modified on the Coordinates and Units tab.

Figure 4 - Loading completion interval data to Petrel as production logs

Upscale Production Logs The next step in the workflow is very important, as the completion information (asproduction logs) must now be upscaled into the model so that model cells which are directly associated withcompletions will have a unique value. As before, it is assumed that the reader is familiar with this process, butFigure 5 shows the basic steps in this process.


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UPSCALING COMPLETION LOGS

Since the completion logs consist of 0 and

1 values, use the Maximum method for

scale-up averaging. Any model cell

intersecting a completion interval will beassigned a value of 1

Before the completion data can beused, the completed intervals(represented by 1 on thecompletion log) must be upscaledinto the Petrel model as a newmodel property using the Scale UpWell Logs process in Petrel.

Figure 5 - Upscaling completion data into the geomodel from production logs

Creating Pseudo-Properties in Geomodel Now that the model cells which intersect completed intervals havebeen designated, the next steps in the workflow populate the model with reservoir-quality values for each propertyto be modified, but only in completed intervals (the remainder of the model is filled with 0 values). Using thePetrel property calculator (Figure 6), the entire model volume is filled with default values in completed intervals and0 for all other model cells.


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CREATING PSEUDO-PROPERTIES

The conditional statement in the propertycalculator sets the model property to the

chosen default in cells intersecting

completions and 0 values for the remainder

of the model volume.

Pseudo-properties are created in

the model using the Petrel

property calculator. Aconditional statement is used to

populate the model with default

property values for completed

zones, and values of 0 where

there are no completions

Figure 6 - Using the property calculator to create pseudo-properties

It is important to choose the default values for each property carefully, as these values can later be used to filterthe model for further quality control and evluation. In this example, for instance, the default value of porosity for acompleted interval is 0.1234. This value was chosen as it is extremely unlikely to occur in reality, but will still beconsidered minimum reservoir quality in this particular reservoir. As discussed in the example later in this paper,the default values can be used as a filter to identify portions of the model where artificial modifications have beenmade.

Modification of Model Properties In the final workflow step, the Petrel property calculator is used to modify theactual model properties. A logic statement compares each cell and replaces the actual property with the pseudo-property if the pseudo-property is greater than the actual property. Since the pseudo-property can only exceed theactual property in completed intervals (and only if the pseudo-property is larger), all the remaining model volumewill be unchanged. Figure 7 is an illustration of this process.


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In most cases, the remodeled property will be visually

indistinguishable from the original property. Remodeled

cells will generally comprise less than 1% of the total modelvolume, as only cells intersecting completion intervals with

values below reservoir quality minimum will be changed

FINAL REMODELED PROPERTY

Figure 7 - Final remodeled property

Application of Methodology

Using this workflow to temporarily modify basic model properties can speed and simplify model initialization. Theimproved reservoir properties should eliminate many simple problems where reservoir properties are marginal,where wells do not have valid petrophysical data or logs, and out-of-zone completions. Of course, this methoddoes not actually correct problems, but in many cases can be used to isolate parts of the model which need furtherreview, or provide a means to correct model problems where conventional modeling approaches cannot be used.The following examples illustrate the practical application of the workflow.

Example 1 Filtering Model to Isolate Possible Problems

In the first example (Figure 8), the model is filtered to show only the areas where corrections to the model propertywere needed to meet minimum reservoir criteria. Using the property settings, the model is filtered by setting thefilter maximum and minimum values equal to the default value for the pseudo-property. When the filter is appliedand visualized in a 3D window, these areas can then be evaluated to determine what corrective action is needed topermanently fix the initial problem.

As discussed earlier, default values should be chosen to represent minimum reservoir quality, while retaining a

unique numerical sequence which is unlikely to occur in nature. In the example, the default value for porosity is0.1234. Since the minimum reservoir-quality porosity is 0.10, the default will slightly exceed the minimum cut-offvalue while still being unlikely as valid log data. Similar values for water saturation (e.g. 0.5678), clay volume(0.4567), or NTG (0.9876) could be used, depending on the actual reservoir cut-off values for reservoir quality.


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APPLICATION OF WORKFLOW

EXAMPLE OF MODEL FILTERING FOR QC

Setting the property filter MIN and MAX valuesto the pseudo-property default value will allow

display of only those portions of the model

which have been modified. This allows much

easier QC of areas of the model where

completion and production data are

inconsistent with petrophysical data

Figure 8 - Filtering to determine portions of the model for review and correction

Example 2 Adjusting Models for Missing or Insufficient Petrophysical Data

Many older fields have wells which have been completed and produced, but do not have sufficient well log data touse in a conventional petrophysical interpretation. Although sophisticated methods using neural networks andsimilar approaches are available to estimate log data in these older wells, we can use this workflow to remodelareas of the property model where log data do not provide accurate property distribution. While this is not asubstitute for a comprehensive petrophysical evaluation, this workflow provides the simulation engineer with aquick means of initializing a model.

In this example (Figure 9), older wells have been completed and produced in a reservoir, but do not have modernlog data to incorporate into the field-wide property distribution. As a result, the statistical distribution of the propertyin the vicinity of these wells yields values which do not meet minimum reservoir cut-off criteria. Although the

property modeling is statistically valid, and honors all the actual well data, there is no simple way to account for thelack of well log control for these wells.


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In this example, two wells

are completed in a reservoir,

but have no valid porosity

logs (old wells).

Although both wells

penetrate cells that are

close to reservoir quality,

they do not meet the

minimum cut-off value (in

this case, 10%)

Property modeling is

statistically valid, but the lackof well control at the

completed wells does not

adjust local properties for

likely reservoir quality (based

on completions and

production historyORIGINAL POROSITY PROPERTY


EXAMPLE OF SIMPLE PROPERTY REMODELING

Figure 9 - Model corrections for missing or invalid log data using simple property remodeling

The first attempt to correct for this situation is to use the simple property remodeling workflow. As shown in Figure10, the resulting model has been adjusted in such a way that the cells penetrated by the completed wells now meetthe minimum reservoir quality criteria.


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REMODELED POROSITY PROPERTY

After remodeling the

porosity property, the cells

associated with the

completed intervals have

been increased to the

default value (which meets

reservoir minimum cut-off

value)

Lack of well control in this

case may require that a

manual local adjustment to

the reservoir quality be

made, or possibly a pseudo-

log approach could be used

to remodel local properties

near the well


EXAMPLE OF SIMPLE PROPERTY REMODELING

Figure 10 - Resulting model using simple property remodeling method

Example 3 Adjusting Models Using a Pseudo-Log ApproachWhile the simple property remodeling method discussed above will eliminate connection errors when a simulationmodel is initialized (in most cases), as the simulation engineer starts comparing model volume with producedvolumes, additional problems will arise in areas of the model where there is insufficient volume to produce theactual produced volumes associated with a completion.

Figure 11 illustrates a situation where several wells (all older wells lacking modern petrophysical log data) arelocated in a portion of the model with very poor reservoir quality, as defined by the actual log data from surroundingwell control. Although these wells have produced significant volumes of hydrocarbons, the simulation model willproduce errors due to the very low model volume in that region.


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ORIGINAL POROSITY PROPERTY

Creating pseudo-logs for

perforated intervals can helpremodel properties in areas

surrounding completions if

indicated by production

history

In this example, three wells

have completions in the

zone, but no valid log data.

A workflow can be used toremodel properties in the

region surrounding the wells

for a more realistic depiction

of local reservoir properties


EXAMPLE OF PSEUDO-LOG REMODELING

Figure 11 - Using pseudo-log remodeling; initial property conditions

The first step in this approach is to apply the simple property remodeling workflow as discussed in the previousexample. As before, the model properties intersected by the wells will be improved (Figure 12), but because theoverall reservoir quality is so low in that region of the model there will undoubtedly be problems with the numericalsimulation. To correct the surrounding areas of the model requires an additional workflow.


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REMODELED POROSITY PROPERTY

Using the workflow

described, the model

properties can be modified in

the cells penetrated by the

completed wells.

unfortunately, production

data indicates that the local

reservoir volume is

insufficient to match

produced volumes.

Another workflow is needed

to modify the properties

locally



Figure 12 - Property adjusted using simple remodeling method

To modify the local model properties in the region near the productive wells, it is necessary to create pseudo-logsfrom the model for the specific wells which need to be modified. The first step in this process is to generatesynthetic logs for the remodeled property using the Petrel Make Logs process. Figure 13 shows the basic steps inthe creation of the synthetic logs from the remodeled property.


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WORKFLOW

Create Remodeled Property Log Upscale Remodeled Property Log

Re-Run Petrophysical Modeling

Right-click on the WELLS folder, open

Settings dialog, select Make Logs tab, select

property to create a log from, and click the

Make Logs button



Figure 13 - Creating remodeled property pseudo-logs

Next, the synthetic logs are upscaled into the model on a selective basis. The Petrel well log scale-up process hasthe option to scale-up logs into an existing property, but the user must be very careful to not to delete the alreadyscaled-up cells from wells with valid petrophysical data. In this example, the three wells are selectively upscaledinto the existing property (Figure 14).


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WORKFLOW

Create Remodeled Property Log

Upscale Remodeled Property Log

Re-Run Petrophysical Modeling

Open the Scale Up Well Logs process, select

the property to be edited, select the individual

wells to upscale, and change settings to leave

existing upscaled cells unchanged

IMPORTANT!

Change these settings toavoid deleting all the real

upscaled cells in the model!



Figure 14 - Upscaling the pseudo-property log from the remodeled property

The final step in the process is to re-run the petrophysical modeling for the entire model. Since only selected wellshave been upscaled (using the remodeled property pseudo-logs), the model will be adjusted in the vicinity of thenewly-upscaled wells, leaving the remainder of the model relatively unchanged. This will produce a final resultwhich will more closely represent model properties by incorporating a combination of actual petrophysical data withpseudo-data justified with historical production data. The final model (Figure 15) shows the local modification tothe property using the pseudo-log approach.


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FINAL POROSITY PROPERTY

Using the pseudo-log

workflow described, the

remodeled properties have

been used to locally modify a

portion of the model

it is important to

remember, however, that

these are pseudo-properties,

and are ONLY to be used

when adjusting simulationmodels where all other data

are lacking!



Figure 15 - Final property model using the pseudo-log remodeling workflow

ConclusionsBased on the assumption that completion intervals should have at least minimum reservoir properties, the workflowpresented utilizes completion data to directly modify geomodel properties in zones with confirmed production. Thisis a very rapid and simple method which can at least temporarily eliminate the majority of initialization errors fornumerical simulation. The resulting modifications can also be used to isolate areas of the geomodel for furtherquality control and correction, or to correct local model properties to adjust the model volumes to better matchvolumes indicated by historical production. However, the workflow does not actually correct problems, it simplypostpones them until more rigorous reservoir characterization and petrophysical corrections can be made.

Acknowledgement

The author would like to thank Tatweer Petroleum for permission to publish and present this material, and for thesuggestions and critiques from Tatweer Subsurface Department staff members for improvements to the workflow

and this manuscript.

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