Welcome to Core Elements # 75! This edition introduces two interesting studies published in the recent issues of AAPG Bulletin – the oldest peer-reviewed research journal in petroleum geoscience published since 1917. This July, Matthew Pranter, who was AAPG Bulletin’s Editor for the past three years, passed the torch on to Daniel Minisini. Daniel and I will be giving an AAPG Academy Webinar on “Geoscience-Energy Nexus: Challenging Times, New Paths” on September 10. Please join us. Also consider submitting your research work to AAPG Bulletin.
Rasoul Sorkhabi
Editor, Core Elements
New Workflow for Direct Hydrocarbon Indicators
Global distribution of ExxonMobil wildcats (gray) since 1994 contained in the database with direct hydrocarbon indicator-supported wells highlighted in red. Base map World Imagery: Earthstar Geographics; World Boundaries and Places: Esri, HERE, and Garmin./AAPG Bulletin
In the May issue of AAPG Bulletin, a group of geoscientists from ExxonMobil reports on their study of “integrated and improved hydrocarbon indicators.” Let’s take a look.
Direct Hydrocarbon Indicators (DHIs) are “anomalous type of seismic amplitude that may occur due to the presence of hydrocarbons.”
Developed by Shell geophysicists in the 1960s, DHI theory and applications have significantly advanced over the years from bright, flat, or dim spots, and amplitude versus offset (AVO) to multi-attribute seismic analysis.
Database: The ExxonMobil researchers used proprietary datasets of wells drilled by their company up to 2023, representing exploration drilling in more than 40 countries.
Geological Chance of Success (GCOS): The first step is to construct GCOS by incorporating a nine-element risk model including:
DHI Evaluation System: The researchers distinguish DHI evaluation system use prior to and after 2021.
Post-2021 system includes quality attributes that move away from absolute metrics to relative/expectation-based metrics. The DHI quality attributes include anomaly strength, lateral amplitude contrast, and amplitude terminations.
Post-2021 system also replaces the expert-guided DHI scoring with a supervised machine learning (SMR) algorithm to “remove human bias.”
DHI discernibility: By integrating GCOS and DHI evaluation via SMR, the researchers built an “integrated chance of success” (iCOS) offering a new metric called “discernibility.”
Discernibility operates on two levels: Expectation and confidence
Discernibility expectation metric determines to what degree DHI observations can be expected based on geological properties, independent of the prospect’s observed DHI attributes. Discernibility expectation metric is rated as likely, more or less likely, and unlikely.
Discernibility confidence metric is the ability to define a reservoir based on the DHI response to the reservoir and fluids. Discernibility confidence metric is rated as high, moderate, low, and No.
Estimated Volume of Oil in Place: The researchers show that DHI observations and discernibility guide volumetric parameters routinely used to estimate oil in place; these include area, gross thickness, net-to-gross ratio, porosity, hydrocarbon saturation, and fluid shrinkage.
Results: The researchers state that the new integrated DHI workflow is
about 50 percent better at discriminating success from failure, and
about 30 percent more accurate than historical DHI workflows.
Why it matters: As oil and gas drilling moves to more challenging prospects, particularly in ultra deepwater basins, integrated DHI and geological observations reduce exploration risks.
(A) Equal Earth projection showing the locations of the four case studies and four reservoirs from the public domain. (B) Flow diagram explaining the preanalysis data scenario and subsequent five-step advanced rock typing road map. (C) The ternary diagram (modified from Skalinski and Kenter, 2014, Figure 3A) reports the relative contribution to flow of three main drivers and shows the reservoir types of four published reservoirs (Saneifar et al., 2015; Skalinski and Kenter, 2015) and the four case studies in this paper./AAPG Bulletin
The April issue of AAPG Bulletin contains an article that offers a new “advanced rock typing” (ART) for characterization of carbonate reservoirs based on petrophysical properties and statistically relevant depositional attributes.
Carbonate Reservoirs, both marine and continental, account for more than 60 percent of the world’s petroleum resources. The Middle East, in particular, contains abundant carbonate reservoirs.
ART Method: The researchers suggest the following steps:
Step 1. Test the influence of lithofacies, diagenesis, and fractures in core-to-log domain scale and set the vertical scale of the rock typing. This step is guided by supervised machine learning.
Step 2. Use electrofacies to generate petrophysical rock categories (PCRs) that integrate data from Step 1.
Step 3. Integrate pore types from laboratory analysis of cores or from borehole images and nuclear magnetic resonance (NMR) logs. Then determine the final PRCS and set the horizontal scale of rock typing.
Step 4. Generate the final PRCs probability cube through kriging interpolation or porosity data and simulate the spatial trends and juxtapositions.
Step 5. Identify depositional or diagenetic drivers that explain the spatial relationships documented in Step 3.
Application: The researchers applied their workflow to the following carbonate reservoirs:
Arab C-D formation (Upper Jurassic), marine ramp/shelf platform deposits, in the Middle East
Thamama B (Kharaib-2) formation (Lower Cretaceous), marine ramp/shelf platform deposits, in the Middle East
Pre-salt formation (Aptian age), lacustrine deposits, in West Africa
What is new:
The ART workflow is a petrophysical data-driven method that captures only statistically relevant geological attributes.
The researchers state that their new ART method has a predictive accuracy of more than 80 percent.
Why it matters:
Unlike siliciclastic reservoirs, minerals such as calcite and dolomite in carbonate reservoirs have metastable behavior in changing subsurface environments, from dissolution, replacement, and reprecipitation.
Advanced workflows for carbonate rock typing, by integrating geological and petrophysical attributes, help characterize these heterogeneous reservoirs on well logs.
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