Thursday, October 11, 2018

Integrated Reservoir Interpretation

Every field is unique , and not just in its geology. Size, geographical location, production history, available data, the field's role in overeall company strategy , the nature of its hydrocarbon  - all these factors determine how reservoir engineers attempt to maximize production potential. Perhaps the only commonality is that decisions are ultimately based on the best interpretation of data. For that task, there is a variability to match the fields being interpreted.


In an oil company with separate geological, geophysical and reservoir engineering departements, the interpretation of data tends to be sequential. Each discipline contributes and then hands over to the next discipline. At the end of the line, the reservoir engineer attempts to reconcile the cumulative understanding of the reservoir with its actual behavior. Isolated from the geologist and geophysicist who have already made their contributions, the reservoir engineer can play with parameters such as porosity, saturation and permeability, but is usually barred, because of practical difficulties, from adjusting the underlying reservoir geometry.


The scenario is giving way to the integrated asset team, in which all the relevant  disciplines work together, hopefully in close enough harmony that each individual's expertise can benefit from insight provided by others in the team. There is plenty of motivation for seeking this route, at any stage of a field's development. Reservoirs are so complex and the art and science of characterizing them still so convoluted, that the uncertainties in exploitation, from exploration to maturity, are generally higher that most would care to admit. 


In theory, uncertainty during the life of a field goes as follows: During exploration, uncertainty is highest. It diminishes as appraisal wells are drilled and key financial decisions have to be mad regarding expensive production facilities- for offshore fields, these typically account for around 40% of the capital outlay during the life of the field. As the field is developed, uncertainty on how to most efficiently exploit it diminishes further. By the time the field is dying, reservoir engineers understand their field perfectly.

A realistic scenario may be more like this: During exploration, uncertainty is high. But during appraisal, the need for crucial decisions may encourage tighter bounds on the reservoir's potential than are justifiable. Later, as the field is developed, production fails to match expectations, and more data, for example  3D seismic data, have to be acquired to plug holes in the reservoir understanding. Uncertainties begin to increase rather than diminish. They may even remain high as parts of the field become unproducible due to water breakthrough and as reservoir engineers still struggle to fathom the field's intricacies.

Asset teams go a long way toward maximizing understanding of the reservoir and placing a realistic uncertainty on reservoir behaviour. They are the best bet for making most sense of the available data. What they may lack, however, are the right tools. Today, interpretation is mainly performed on workstations with the raw and interpreted data paraded in its full multidimensionality on the monitor. Occasionally, hard-copy output is still the preferred medium - for example, logs taped to walls for correlating large numbers of wells. 

There are workstation packages for 3D seismic interpretation, for mapping, for viewing different parts of the reservoir in three-dimensions, for petrophysical interpretation in wells, for performing geostatistical modeling in unsampled areas of the reservoir, for creating a grid for simulation , for simulating reservoir behavior, and more.  But for the reservoir manager, these fragmented offerings lack cohesion. In a perceived absence of an integrated reservoir management package, many oil companies pick different packages for each spesific application and then connect them serially.

Any number of combinations is possible. The choice depends on oil company preferences, the history of the field and t he problem being adressed. Modeling a mature epehant field in the Middle East with hundreds of wells and poor seismic data may require a different selection of tools than a newly discovered field having high-quality 3D seismic coverage and a handful of appraisal wells. Reservoir management problems vary from replanning an injection strategy for mature fields, to selecting horizontal well for optimum recovery , to simply estimating the reserves in a new discovery about to be exploited.

Whatever the scenario, the tactic of stringing together diverse packages creates several problems. First is data compatibility. Since the industry has yet to firm up a definitive geoscience data model, each package is likely to accept and output data in slightly different ways. This forces a certain amount of data translation as the interpretation moves forward-indeed, a small industry has emerged to perform such translation. Second, the data management system supporting this fragmented activity must somehow keep track of the interpretation as it evolves. Ideally, the reservoir manager needs to know the history of the project, who made what changes, and if ncessary how to backtrack. 

Postprocessing Seismic Data

The interpretation path obviously depends on the data available. For two of the three fields considered here, there were excellent 3D seismic data. And in all three fields, there was at least one well with a borehole seismic survey. The first goal in working with seismic data is to ensure that the borehole seismic and the surface seismic at the borehole trajectory look as similiar as possible. If that is achieved, then the surface seismic can be tightly linked to events at the borehole and subsequently used to correlate structure and evaluate properties between wells. If no borehole seismic data are avaialble, an alternative is to use synthetic seismograms, computed from acoustic and density logs.

Differences in seismic data arise because of difficulties in achieving a zero phase response, a preferred format for displaying seismic results in which each peak on the trace corresponds exactly to an acoustic impedance contrast, and , by inference, geologic interface. Processing seismic data to obtain a zero-phase response depends on accurately assessing the signature of the acoustic source. This is straightforward in borehole seismics because t he measured signal can be split into its downgoing and upgoing componentss, and the former yields the source signature. In surface seismics, the downgoing field is unmeasured and statistical techniques must be used to assess the signature, leading to less reliable results.  In Conoco Indonesia, Inc.'s field, the surface seismic and borehole seismic data initially matced poorly. With the residual processing module, the mismatch is resolved by comparing the frequency spectra of the two data sets and designing a filter to pull the surface seismic data into line with the borehole seismic data. In this case, the postmatch alignment is excellent.






 However, if the alignment resulting from this treatment remains poor, it may prove necessary to vary the design of the filter versus two-way time. This is achieved by contructing filters for several specific time intervals along the well trajectory and then interpolating between them to obtain a representative filter at any two-way time. 

 The next step is to perform a seismic inversion on the matced seismic data, using the inversion module. This process converts the matced seismic data to acoustic impedance, defined as the product of rock density and acoustic velocity. Acoustic impedance can be used to classify lithology and fluid type. Mapped across a section in two dimensions or throughout space in three dimensions, acoustic impedance provides a valuable stratigraphic correlation tool. For Conoco Indonesia, Inc., inversion provided valuable insight into the lateral extent of the reservoir. 

The inversion computation requires the full spectrum of acoustic frequencies. Very low frequencies are missing from the seismic record, so these are estimated interactively from acoustic well logs. Between wells, this low-frequency information is interpolated  , and for a 3D inversion, the information must be mapped everywhere in reservoir space.

 

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