Predict permeability for uncored wells
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ADNOC has developed a new tool called "PERM42" to predict permeability in uncored wells. PERM42 can use 1-6 well log parameters to train models and predict permeability where there are no core samples. It was tested on four wells where it closely matched actual core permeability measurements. Permeability modeling is crucial but difficult, consuming most of the time in reservoir characterization. ADNOC hopes PERM42 can improve predictions and streamline the process.
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Air pollution is contamination of the indoor or outdoor environment by any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere.
Household combustion devices, motor vehicles, industrial facilities and forest fires are common sources of air pollution. Pollutants of major public health concern include particulate matter, carbon monoxide, ozone, nitrogen dioxide and sulfur dioxide. Outdoor and indoor air pollution cause respiratory and other diseases and are important sources of morbidity and mortality.
WHO data show that almost all of the global population (99%) breathe air that exceeds WHO guideline limits and contains high levels of pollutants, with low- and middle-income countries suffering from the highest exposures.
Air quality is closely linked to the earths climate and ecosystems globally. Many of the drivers of air pollution (i.e. combustion of fossil fuels) are also sources of greenhouse gas emissions. Policies to reduce air pollution, therefore, offer a win-win strategy for both climate and health, lowering the burden of disease attributable to air pollution, as well as contributing to the near- and long-term mitigation of climate change.
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- 1. PERM42 A New Technology to Predict Reservoir Permeability for Uncored Wells
- 2. Petrophysical Modeling (Permeability) 10-20% Cored Wells 80-90% Uncored Wells ADNOC Permeability Prediction Model Blind Test Permeability Prediction well # 1 well # 2 well # 3 well # 4 Core Perm Predicted Perm
- 3. Petrophysical Modeling (Permeability) 10-20% Cored Wells 80-90% Uncored Wells ADNOC Permeability Prediction Model Blind Test Permeability Prediction well # 1 well # 2 well # 3 well # 4 Core Perm Predicted Perm Permeability modeling is the most challenging task in any Reservoir Characterization Work Over 90% of the time consumed during iteration process between Static & Dynamic models is due to Permeability Model Adjustment ADNOC has build its own permeability prediction tool that showed encourage results
- 4. Permeability-Porosity Relationship, Case Example 14,792 data set 265,772 data set PERM42, has been designed to use from 1 to 6 logs parameters for data training to predict permeabilitys for uncored depth intervals. In the following example, only Porosity and Sw logs as input data were used.
- 5. Core & Predicted Permeabilitys Distribution Trend
- 6. Core & Predicted Permeabilitys per Well, Case Example PERM42, shall honor all intervals that have core permeability measured values, and it would generate permeability values for uncored interval, while respecting barriers and high permeability streaks.