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W
ithin a field evaluation, seismic events, such as flat spots, amplitude anomalies, or other seismic-derived attributes are often used in the interpretation process. When these seismic events are demonstrated to be robust enough, consistent with well data and the geologic scenario, they can be qualified as direct hydrocarbon indicators (DHIs) and used as key elements to assess fluid contacts, reservoir extension, and compartmentalization.This paper presents three examples of DHI integration into resources evaluation, thanks to a confidence assessment method- ology necessary to evaluate the DHI robustness and certainty. The DHI confidence assessment includes two complementary evalu- ations:
• The first one assesses the seismic and petrophysical data qual- ity, and their ability to properly represent the seismic response for the known (or expected) reservoir and fluid characteristics. It analyzes the DHI detectability and its type according to dif- ferent geologic contexts (burial, lithology variations, etc. ); • The second one evaluates the consistency of the geophysical
information with the geologic and dynamic knowledge of the field including their uncertainties. This “cross-view” is funda- mental in the process, as the consistency between independent information ranging from exploration/delineation well results (reservoir properties, fluid saturation, pressures, DST, etc.) to field production behavior is the key to confidence.
Three examples extracted from our analog database will be reviewed to illustrate in different application contexts (contact definition, panel connectivity evaluation) the DHI input for the field evaluation through the confidence assessment methodology. They illustrate the necessity of an integrated approach which goes beyond the DHI technical aspects to ensure geologic scenarios consistency associated to certainty criteria.
This DHI assessment as well as the quality control it infers can represent a significant aspect of the geophysical input to the field evaluation which also has to address other characteristics of
Pierre-Louis Pichon, sabine DeLahaye, GreG Fabre, and PascaL DeseGauLx, Total SA
the field such as its structural shape, the accumulation perimeter definition, the reservoir geometry and quality, and other elements impacting the static and dynamic evaluations, along with the as- sociated reserves-resources.
A DHI evaluation workflow is used in Total to assess the seis- mic and petrophysical data quality and evaluate the consistency of all available data. This approach goes well beyond geophysics and integrates all available data to evaluate the interpretation con- sistency and associated uncertainties.
In the first part of this article, the workflow will be described with the focus on the main steps and elements of both DHI qual- ity and overall field consistency evaluations. It will then be illus- trated through three practical examples.
DHI confidence assessment workflow
It is important to first define a confidence assessment workflow to ensure a comprehensive and systematic approach. It provides the seismic interpreter and the geosciences evaluation team with guidelines to ensure reliable work (and a thorough review of the data that emphasizes interpretation consistency and avoiding pitfalls). This workflow can be subdivided into two main assess- ment steps: DHI quality and overall hydrocarbon accumulation consistency.
DHI quality assessment
Using a DHI necessitates first evaluating the seismic data quality in terms of reliability, adequacy to reservoir characteristics, and limitations of use. The acquisition and processing parameters must be reviewed according to objectives. Extensive data quality checks are performed to evaluate the related uncertainty and rec- ommend improvements when necessary. This step is fundamen- tal in order to get a clear view of the advantages and weaknesses of the seismic data which will be interpreted.
Petro-elastic data also must be fully analyzed starting from the quality control and accurate editing of the logs, the reliability of petro-elastic models, and of multiscenario fluid substitutions are evaluated.
Then the well-to-seismic calibration (including prestack) is analyzed in detail; input data and methods used must be fully documented. This step may impact the processing scheme itself if performed early in the sequence.
The matching quality indicates how reliably the seismic in- formation can be interpreted through a smaller scale petro-elastic behavior.
A DHI corresponds to a specific attribute response within the seismic data sets. But frequently, for its analysis, this information
Figure 1. Example 1. Contacts definition from seismic cross-section and attribute map.
Editor's note: This article was expanded by the authors from "DHI confidence assessment for field evaluation: An integrated geosciences necessity," SEG Expanded Abstracts 30, 1140 (2011), doi: http://dx.doi. org/10.1190/1.3627404.
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amplitude dimming/brightening expected to correspond to a flu- id contact should usually be at a constant depth for a given panel, within the time-to-depth conversion uncertainty. Its extension should be consistent with the reservoir limits and top depth map to contact. Additionally, it must be consistent with the trapping history (underfilled structure, spill point, stratigraphic trapping, sealing faults, etc.).
It also must be challenged with other geologic data from wells, sedimentary models, and regional knowledge such as reservoir fa- cies heterogeneity, spatial extension and thickness, compaction trends, reservoir continuity across faults, diagenesis, etc.
Pressure and fluid information have to be considered: depth is extracted from 3D cubes using interpreted surfaces. The ad-
equacy of such extraction also needs to be assessed.
As a result, the DHI quality is controlled in terms of charac- terization, representativeness, reliability and uncertainties, in both geophysical and petrophysical domains.
Overall accumulation consistency assessment
The second step aims at evaluating the consistency of the DHI information and related uncertainties with other independent geoscientific knowledge.
First, the DHI must be consistent with other aspects of seis- mic interpretation, from structural to sedimentary; a flat spot or
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of fluid contacts and related uncertainties, connectivity between reservoirs and compartments, fluid characteristics homogeneity, PSAT uncertainties versus GOC, seal retention capacity versus hydrocarbon column, etc.
Production test or history matching results will also provide constraints on possible contacts and flowing limits. 4D seismic, when available, provide useful information on the definition of the flow units, compartmentalization, fluid movements, etc.
By compiling all these independent analyses, a robust under- standing can be achieved, with associated uncertainties. Alterna- tive geological scenarios also must be investigated, and ranked in term of probability of occurrence. Thanks to this thorough analy- sis, the DHI can be integrated with the proper level of certainty to the global field evaluation.
Contacts evaluation: Example 1
This first example corresponds to a clastic reservoir drilled by a unique well.
In the upper interval of the reservoir, well GF-1 encountered a gas-oil contact, which shows an excellent conformance to the structure (Figure 1). However, the oil-bearing part of the reservoir is defined only by an “oil down to” within both upper and lower reservoir intervals. The objective is to estimate the depth of the water-oil contact using available information and to define the associated degree of confidence.
The seismic quality is good; it is a high-resolution marine seismic, prestack-migrated in time with a fourth-order velocity correction applied (ensuring long-offset reliability), and exempt of imaging issues at the objective. It allows a detailed structural and sedimentary interpretation, with a resolution greater than reservoir thickness and a reliable time/depth conversion model calibrated at wells.
A clear petro-elastic difference between oil and water sands is evident:
• Fluid substitutions based on well logs allow the modeling of oil-bearing and water reservoir responses. The match with the actual seismic response (including GOC) is excellent.
• AVO calibration from nearby wells gives confidence to fluid contact interpretation.
A detailed interpretation of both upper and lower reservoirs shows that they are connected together, and the seismic attribute responses of those reservoirs are coherent with both the petroelas- tic modeling and the sedimentary/structural reservoir extension interpretation.
Additionally, the conformance between the shutoff of the calibrated seismic attributes (substacks, AVO, inversion) and an iso-depth line is clearly seen (Figure 2), within both upper and lower reservoir extensions and consistent with the spill limits.
The upper and lower reservoir connectivity as well as the DHI depth are confirmed by the GF-1 WFT pressure gradient, the interpretation of its oil-pressure trend, and intersection with the regional aquifer trend.
The quality of the DHI response and consistency between independent information (pressure data, depth conversion, sedi- mentary interpretation, etc.) provides a high confidence to the WOC depth estimate. The corresponding hydrocarbon volumes may therefore qualify as reserves assuming all other criteria are met.
Compartmentalization evaluation: Example 2
On the second example, well-1 drilled a turbiditic channel and encountered a stack of several oil-bearing sands. A WOC was recognized at the well and confirmed with pressure measure- ments. All sands are vertically connected.
The seismic has good resolution with a high signal-to-noise ratio. The seismic-to-well calibration of the drilled channel and encountered WOC is good. Several nearby wells can be used to model the seismic responses for different fluids and net-to-gross ratios, thanks to a thorough petro-elastic study.
Two panels, B and C on Figure 3, are fault-separated from the main one (A) which is drilled. Within the turbiditic channel extension, all panels show the same amplitude and a mix of sedi- mentary and fluid responses.
Figure 3. Example 2. Compartmentalization analyses on seismic channel random line and amplitude map.
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