Interview Questions for Log Measurement

Open-hole logging involves lowering various sensors down an uncased borehole to measure the physical properties of the formations surrounding the well. These measurements provide crucial subsurface information for petroleum exploration and production. The principle lies in the interaction of the logging tool’s emitted signals (e.g., acoustic waves, gamma rays, electrical currents) with the rock formations. The response of the formation to these signals is then recorded and analyzed to determine properties like porosity, permeability, lithology, and fluid content.

Imagine it like shining a flashlight into a dark cave – the way the light reflects or is absorbed tells you something about the cave’s walls. Similarly, open-hole logging uses different types of ‘flashlights’ (logging tools) to ‘see’ into the subsurface and gain insights into its composition.

Resistivity, porosity, and density logs all provide different but complementary information about the subsurface formations. They’re like three different perspectives on the same geological puzzle.

• Resistivity logs measure the ability of the formation to resist the flow of electrical current. High resistivity indicates the presence of hydrocarbons (oil and gas), which are poor conductors. Low resistivity suggests the presence of conductive fluids like water.
• Porosity logs measure the volume of pore space within a rock formation. High porosity generally indicates a greater potential for hydrocarbon storage. Different logging techniques, like neutron and density logs, are used to estimate porosity.
• Density logs measure the bulk density of the formation. This information is combined with porosity logs to determine the matrix density (density of the rock itself), which helps in identifying lithology (rock type) and fluid type.

For example, a high resistivity log combined with a high porosity log and a relatively low density log could indicate a gas-bearing sandstone formation. The gas reduces density and increases resistivity compared to water saturation.

Identifying gas zones using log data relies on recognizing the unique signature gas leaves on various logs. Gas has lower density and higher resistivity than water or oil. Several combinations of log responses can indicate the presence of gas:

• Low density/high porosity: Gas is less dense than water or oil, causing the density log to register lower values than expected for the porosity measured.
• High resistivity: Gas is a very poor conductor of electricity, thus leading to higher resistivity readings compared to water-saturated formations.
• Neutron-density crossover: In certain scenarios, the neutron porosity log reading will be higher than the density porosity log reading. This crossover is a classic indicator of the presence of gas. The neutron log is more sensitive to hydrogen, which is present in hydrocarbons.

It’s important to note that cross-plotting different log data (e.g., density versus neutron porosity) is often necessary to reliably identify gas zones. The interpretation also requires careful consideration of the geological context.

Every logging tool has limitations. These stem from the physical principles on which they operate and environmental factors influencing the measurements. Here are a few examples:

• Resistivity logs: The accuracy can be affected by borehole conditions (e.g., mud invasion, borehole diameter variations), formation anisotropy (different resistivity in different directions), and the presence of conductive minerals.
• Porosity logs (Neutron): Affected by borehole size, mudcake thickness, formation salinity, and presence of high-density minerals (e.g., barite).
• Density logs: Affected by borehole size, mudcake thickness, and presence of high-density minerals. They struggle in very low-density formations.
• Gamma ray logs: While generally robust, they can be affected by tool calibration errors, borehole conditions, and the presence of radioactive minerals that are not related to shale content.

Understanding these limitations is crucial for accurate log interpretation. Geologists and engineers use multiple log types and techniques to mitigate these limitations and ensure better results.

Log calibration is the process of establishing a relationship between the measured raw signal and the actual physical property being measured. It ensures that the log data accurately reflects the subsurface formation properties. This involves using known standards or reference materials.

Imagine a bathroom scale – before you can weigh yourself accurately, it must be calibrated to zero with no weight on it. Similarly, logging tools need to be calibrated so that their readings are consistent and accurate. This might involve comparing the tool’s readings in a known environment, such as a laboratory calibration facility, to establish a correction factor. This factor is then applied to field readings to compensate for instrument drift or other factors affecting the measurements. Without calibration, the log data would be unreliable and lead to wrong interpretations.

Gamma ray logs measure the natural radioactivity of formations. This radioactivity is primarily associated with the clay content of the rocks. High gamma ray counts typically indicate shale (clay-rich) formations, while low counts suggest sandstone or carbonate rocks.

The interpretation involves analyzing the gamma ray log curve. A high gamma ray reading corresponds to a shale bed, while a low gamma ray reading may indicate a sandstone bed. The amplitude of the gamma ray readings provides information about the amount of shale present. Gamma ray logs are invaluable for identifying shale content, determining lithology, and correlating different wells.

For example, a sudden spike in the gamma ray log could suggest a shale layer acting as a seal or caprock for a reservoir.

Neutron porosity logs measure the hydrogen index of a formation. Since hydrogen is predominantly found in water and hydrocarbons, the neutron log provides an indirect measure of porosity. A neutron source emits neutrons into the formation, and detectors measure the number of neutrons that return to the tool. The number of returned neutrons is inversely proportional to the hydrogen index.

High porosity formations generally have a higher hydrogen index and thus will show lower neutron log readings (more neutrons are slowed down or captured by hydrogen atoms). Low porosity formations will show higher neutron log readings (fewer neutrons are captured). Neutron porosity logs are particularly useful in identifying porosity in formations with low density matrix, which can be challenging for density logs.

However, it’s important to remember that neutron logs are sensitive to the type of fluid filling the pores (e.g., gas, oil, water) and that this sensitivity needs to be considered during the interpretation. It’s often combined with other log types for a more comprehensive analysis.

Density logs measure the bulk density of the formation. This is achieved by emitting a gamma ray source and measuring the backscattered gamma rays. Denser formations attenuate (reduce the intensity of) more gamma rays, resulting in a lower count rate. The principle lies in the relationship between the formation’s density and its ability to absorb gamma radiation. Think of it like throwing pebbles at a wall – a denser wall will stop more pebbles.

Applications: Density logs are crucial for several reasons. Firstly, they’re fundamental in calculating porosity. Porosity is the percentage of void space in the rock, and we can determine it by knowing the density of the rock matrix (e.g., sandstone, limestone) and the measured bulk density. Secondly, density logs help identify lithology (rock type) by comparing measured density to known densities of different rock types. Finally, they are instrumental in calculating hydrocarbon saturation, especially when combined with neutron logs.

Example: A density log showing a consistently high density in a certain depth interval could indicate the presence of a dense formation like limestone, whereas a lower density might suggest a sandstone formation or a zone with high porosity.

Water saturation (Sw) refers to the fraction of pore space filled with water. We can’t directly measure it from a single log, but we can derive it using various log combinations. The most common method uses the Archie’s equation:

Where:

• Sw = Water Saturation
• n = Cementation exponent (typically between 1.5 and 2.5)
• a = Tortuosity factor (typically between 0.8 and 1.0)
• Rw = Resistivity of formation water (measured or estimated)
• ∅ = Porosity (from density or neutron logs)
• m = Porosity exponent (typically around 2)
• Rt = True formation resistivity (from resistivity logs)

This equation shows the relationship between resistivity, porosity, and water saturation. A high resistivity indicates a low water saturation (more hydrocarbons) and vice-versa. Accurate estimation requires careful selection of the Archie’s parameters (a, m, n) based on the reservoir characteristics.

Practical Application: In exploration and production, determining water saturation is critical for assessing the hydrocarbon reserves and identifying potentially productive zones. Low Sw values indicate hydrocarbon-bearing formations.

Resistivity logs measure the ability of a formation to conduct or resist the flow of electrical current. Different tools provide various measurements and are sensitive to different investigation volumes.

• Induction logs: These are commonly used in high resistivity formations. They transmit an alternating electromagnetic field and measure the induced current to infer resistivity. They have good vertical resolution and are less affected by borehole conditions compared to other tools. They are ideal for exploration and characterization of hydrocarbon-bearing formations.
• Laterologs: These tools use a focused current to measure resistivity, making them less sensitive to borehole effects. They penetrate deeper into the formation compared to induction logs and are effective in formations with invaded zones.
• Microresistivity logs: These are used to measure resistivity very close to the borehole. They are sensitive to the invasion profile of drilling mud, providing information on the permeability and extent of fluid invasion. They help in characterization of thin beds and thin layers close to the borehole.
• SP logs (Spontaneous Potential): Although not strictly a resistivity log, the SP log provides information about the permeability and salinity differences between the formation and the drilling mud. The SP curve deflects in response to permeable and shale-bearing formations which help us understand the geology of the area.

Application: Resistivity logs are essential in identifying hydrocarbon-bearing formations because hydrocarbons are poor conductors of electricity. High resistivity readings typically indicate the presence of oil or gas.

Shale volume (Vsh) represents the proportion of shale in a rock formation. Shale is a low-porosity, low-permeability rock that can significantly impact reservoir quality. Several methods exist to calculate Vsh, often using log responses which are sensitive to the presence of shale.

• Gamma Ray Log: The most common method utilizes the gamma ray log. Shales generally exhibit higher natural radioactivity than other rock types. Therefore, a high gamma ray reading often suggests a higher shale volume. The Vsh is calculated by normalizing the gamma ray readings to a known shale baseline.
• SP Log: The SP log can also be used to estimate shale volume based on the deflection of the curve. Shales typically cause a larger deflection of the SP curve than other rock types.
• Neutron-Density Porosity Difference: The difference between neutron porosity and bulk density porosity can be used to estimate Vsh, as shale generally has a larger neutron porosity than its bulk density porosity.

Each method has its limitations and requires careful calibration and consideration of local geological conditions. Often, a combination of methods is used to get a more reliable estimate of shale volume.

Example: A gamma ray log showing high counts in a specific interval will suggest a higher Vsh in that interval, indicating a shaly layer which may not be productive.

Borehole effects refer to distortions of log readings caused by the presence of the borehole itself. Factors like borehole diameter, mud cake, and invaded zone significantly impact measurements. Compensation techniques are employed to correct for these effects.

• Environmental Corrections: These corrections account for the effects of borehole diameter, mud resistivity, and mud cake thickness. Many modern logging tools have built-in corrections based on measurements from other tools.
• Tool-Specific Corrections: Specific corrections are applied based on the type of logging tool used. For instance, induction log corrections account for the borehole’s impact on the electromagnetic field.
• Advanced Processing Techniques: Sophisticated software uses algorithms to model the borehole and its effects on log readings, providing a better estimation of the true formation properties. For instance, some software helps correct for shoulder beds which interfere with the readings, increasing accuracy.

Example: A large-diameter borehole can significantly reduce the apparent resistivity of the formation, leading to underestimation of hydrocarbon saturation. Proper borehole corrections are crucial to obtain accurate interpretations.

Log data quality control (QC) is a crucial step in ensuring the reliability and accuracy of log interpretations. It involves several steps:

• Visual Inspection: Initially, logs are visually inspected for obvious anomalies like spikes, jumps, or unusual patterns which can result from tool malfunction or other issues. It’s like proofreading a document for typos.
• Calibration Checks: The calibration of the logging tools is verified to ensure they were functioning correctly during data acquisition. This involves comparing the readings against known standards or referencing calibration curves.
• Depth Matching: Logs from different tools are carefully checked to ensure they are accurately aligned in depth. Inconsistent depth alignment can cause spurious interpretations.
• Cross-Plot Analysis: Logs are cross-plotted to identify inconsistencies or relationships that deviate from expected geological behavior. This can help highlight potential errors or unusual formations.
• Statistical Analysis: Statistical methods can be used to identify outliers or anomalies in the data which may reflect inaccurate measurements. For example, Z-score analysis or outlier detection using various algorithms can be employed.

Practical Application: Rigorous QC is essential to avoid misinterpretations that could lead to costly errors in exploration and production decisions. Poorly processed logs can lead to inaccurate estimations of reserves and wrong decisions on well completions.

Integrating log data with other geological data provides a more comprehensive understanding of the subsurface. Various methods are employed depending on the available data:

• Core Data Correlation: Log data can be correlated with core descriptions and laboratory measurements (e.g., porosity, permeability, grain size analysis) from core samples which help verify and refine log interpretations.
• Seismic Data Integration: Seismic data provide a broad view of the subsurface structure. Integrating seismic attributes with log data improves the definition of geological layers and reservoir characteristics.
• Geological Models: Log data are often used to create geological models that help visualize and quantify reservoir properties. These models serve as a foundation for reservoir simulations and production planning.
• Petrophysical Modeling: Log data are integrated using petrophysical modeling tools (software) to predict reservoir properties in uncored sections of the well, giving more insights into the reservoir.
• Geochemical Data Integration: Integration of geochemical data (such as elemental composition) from core analysis and cuttings provides added information on reservoir formation and composition, helping in creating comprehensive models.

Example: Seismic data might show a large geological structure, while logs define the lithology, porosity, and fluid saturation within that structure, leading to a more accurate assessment of the reservoir’s potential.

Acoustic logs measure the speed of sound waves traveling through the formation. This speed, or interval transit time (Δt), is directly related to the rock’s properties. Faster sound velocities generally indicate denser, more consolidated formations, while slower velocities suggest softer, more porous rocks. We use this information to determine things like lithology (rock type), porosity, and even the presence of fractures.

For example, a high Δt value might suggest a sandstone formation with high porosity, which could be a potential reservoir rock. Conversely, a low Δt might indicate a shale or a highly consolidated rock with lower porosity.

In practice, acoustic logs are essential in well logging for identifying potential hydrocarbon reservoirs and determining the mechanical properties of the rock, which is crucial for drilling and completion planning.

Nuclear Magnetic Resonance (NMR) logs measure the response of hydrogen nuclei (protons) in pore fluids to a magnetic field. This response provides detailed information about pore size distribution, porosity, and fluid type. The key parameters derived from NMR logs are T₂ distribution (decay time of the signal, indicating pore size), porosity (total amount of pore space), and free fluid index (FFI).

Interpreting NMR logs involves analyzing the T₂ distribution curve. Different peaks or regions on this curve correspond to pores of different sizes. A broad T₂ distribution typically indicates a wide range of pore sizes, which can affect fluid flow and reservoir quality. For example, a large peak at high T₂ values signifies the presence of large pores filled with mobile hydrocarbons, which are easily produced.

Imagine it like this: Think of a sponge. NMR tells us not only how much water the sponge can hold (porosity) but also the sizes of the holes in the sponge. Big holes (large T₂) allow water to flow easily; small holes (small T₂) hold water more tightly.

A composite log is a combination of different log curves displayed together to provide a comprehensive overview of the formation. The process involves carefully selecting relevant logs based on the well’s objectives. These logs can include resistivity, porosity, density, neutron, acoustic, and other relevant measurements.

Creating a composite log involves several steps:

• Data Acquisition: Gather the necessary log data from the well.
• Data Quality Control: Ensure the data is accurate and clean, correcting any errors or inconsistencies.
• Log Calibration: Standardize the scales and units of different log curves to facilitate easy comparison.
• Curve Selection: Choose relevant logs that best address the specific geological questions. For example, to determine lithology, you might use the gamma ray, neutron, and density logs.
• Log Overlay: Display the selected logs side-by-side in a single display, often with a depth scale as a common reference.
• Interpretation: Analyze the combined information from different log curves to make geological interpretations.

The final composite log provides a clearer, more integrated picture of the subsurface than any single log in isolation, facilitating better reservoir characterization and decision-making.

Interpreting logs from different lithologies requires understanding the unique response of each rock type to different logging tools. For example, sandstone typically exhibits high porosity on neutron and density logs, and relatively high resistivity, depending on the pore fluid. Shale, on the other hand, usually shows low resistivity and low porosity. Limestone and dolomite exhibit different responses depending on their porosity and cementation.

We use cross-plots of various logs (e.g., density vs. neutron porosity) to identify different lithologies. Each lithology will cluster in a specific region on the cross-plot due to its unique physical properties. Furthermore, a well-calibrated gamma ray log is fundamental in identifying shale formations. The gamma ray log measures the natural radioactivity of the formation, with higher values usually indicating the presence of shale.

For example, a high gamma ray log coupled with low porosity logs would suggest a shale formation, whereas high porosity and low gamma ray values might indicate a clean sandstone reservoir.

Micro-resistivity imaging logs provide high-resolution images of the borehole wall, revealing details about the formation’s structure, fractures, and bedding planes. These logs employ numerous electrodes to measure the resistivity of the formation at very high spatial resolution, effectively generating an image of the borehole wall.

The images generated are invaluable for identifying and characterizing geological features such as:

• Fractures: Their orientation, density, and conductivity (related to fluid content).
• Bedding Planes: These can impact fluid flow and permeability.
• Permeability Variations: Differences in resistivity can suggest variations in permeability within the formation.
• Wellbore Stability: Identifying unstable zones prone to collapse.

Imagine it like taking a high-resolution photograph of the borehole wall. This allows for a much more detailed understanding of the formation’s properties than conventional resistivity logs.

Interpreting logs from deviated wells (wells that are not vertical) presents several challenges. The main challenge stems from the fact that the logging tools are not always perpendicular to the bedding planes. This can lead to inaccurate measurements of formation properties, especially porosity and permeability.

These challenges include:

• Tool Eccentricity: The tool may not be centered in the wellbore, leading to biased measurements.
• Bed Boundary Effects: The inclination of the well can cause the tool to sample formations at different angles, making it difficult to correctly assess bed thickness.
• Anisotropy Effects: Formations often exhibit anisotropic properties (different properties in different directions). Deviations can make these directional properties more difficult to measure and interpret.
• Environmental Corrections: More complex corrections are required to account for the tool’s position and the borehole’s geometry.

Sophisticated software and advanced processing techniques are required to correct for these effects and obtain more accurate interpretations from logs in deviated wells. For example, some processing techniques involve rotating the log data to align them with the formation’s bedding planes.

Log data forms the bedrock of reservoir characterization, providing crucial information about the reservoir’s properties. We use this information to estimate key parameters such as porosity, permeability, water saturation, and lithology.

The process involves:

• Porosity Determination: Neutron, density, and sonic logs are used to estimate total and effective porosity.
• Permeability Estimation: While logs don’t directly measure permeability, we can estimate it using empirical correlations that relate permeability to porosity and other log parameters. NMR logs provide the most direct measure of permeability-related properties.
• Water Saturation Calculation: Resistivity logs are used, along with Archie’s equation or similar empirical relationships, to estimate water saturation (the fraction of pore space filled with water).
• Lithology Identification: Crossplots of various logs are used to identify lithology, and the gamma ray log helps to identify shale volumes.
• Reservoir Geometry Definition: Logs, combined with seismic and other data, help to define the reservoir’s boundaries and geometry.

This comprehensive information allows for detailed reservoir modeling, ultimately supporting critical decisions regarding reservoir management and hydrocarbon production optimization. For example, identifying high-permeability zones helps optimize well placement and completion strategies, maximizing production.

Logging while drilling (LWD) data provides real-time information about the subsurface formations as the well is being drilled. Unlike wireline logging, which is performed after drilling, LWD allows for immediate analysis and decision-making. This data is crucial for optimizing drilling operations and understanding reservoir properties.

• Formation Evaluation: LWD tools measure properties like porosity, density, resistivity, and gamma ray, providing an early assessment of potential hydrocarbon reservoirs.
• Drilling Optimization: Real-time data on formation strength and pressure allows for adjustments to drilling parameters, minimizing risks such as wellbore instability or unexpected pressure changes.
• Reservoir Characterization: LWD data helps in understanding reservoir architecture, identifying fluid contacts (e.g., oil-water contact), and estimating hydrocarbon saturation.
• Horizontal Well Steering: In horizontal drilling, LWD tools guide the drill bit to stay within the productive reservoir zone, maximizing hydrocarbon recovery.

For example, if LWD resistivity data indicates a high resistivity zone, this suggests a potential hydrocarbon bearing formation, prompting further investigation and potentially influencing decisions about well placement and completion strategy.

Generating a petrophysical model involves integrating various data sources to create a three-dimensional representation of the subsurface reservoir. This model is essential for reservoir simulation and production forecasting.

1. Data Acquisition: This begins with gathering log data (wireline and/or LWD), core analysis data, and pressure measurements. Other data such as seismic information and production tests are often integrated to improve the model’s accuracy.
2. Log Analysis: Raw log data is processed and interpreted to estimate key petrophysical parameters, including porosity, water saturation, permeability, and lithology.
3. Data Integration: Core analysis data, providing laboratory measurements of reservoir properties, is integrated with the log data to improve the accuracy of the log interpretation. This ensures consistency and helps calibrate the log-derived parameters.
4. Model Building: The interpreted data is used to construct a three-dimensional geological model, incorporating structural features, fault lines, and stratigraphic variations. This model is often built using specialized software.
5. Model Validation: The model’s accuracy is validated by comparing its predictions with production data and other available reservoir information. This may necessitate iterative refinement of the model.

Imagine building a 3D puzzle of the subsurface: each piece represents a specific data point, and the final assembled puzzle is the complete petrophysical model showing the distribution of reservoir properties in 3D.

Log data is fundamental in evaluating hydrocarbon reserves. Various log types provide information necessary to calculate the volume of hydrocarbons in place (stock tank oil initially in place, STOIIP; gas initially in place, GIIP).

• Porosity Determination: Logs like density, neutron, and sonic logs measure the pore space in the rock, providing porosity estimates. High porosity indicates greater potential for hydrocarbon storage.
• Hydrocarbon Saturation: Resistivity logs measure the electrical conductivity of the formation. High resistivity indicates the presence of hydrocarbons (which are poor conductors), while low resistivity suggests the presence of water (a good conductor).
• Volume of Rock: The area and thickness of reservoir intervals are determined from logs and other geological data. This area times thickness calculation provides the volume of the reservoir rock.
• Hydrocarbon Type: While not directly measured by logs, the combination of other log data, pressure data and fluid samples indicates the type of hydrocarbon (oil, gas, or condensate).

Using these parameters, engineers employ equations (such as the volumetric method) to estimate the total hydrocarbon volume in the reservoir. For example: STOIIP = 7758 * A * h * Φ * (1 - Sw) * Bo where A is the area, h is the reservoir thickness, Φ is the porosity, Sw is the water saturation, and Bo is the formation volume factor of oil.

I have extensive experience with various log analysis software packages, including Schlumberger’s Petrel and Techlog, and Halliburton’s Landmark OpenWorks. My expertise covers data import, quality control, log editing, interpretation and reporting functionalities in these platforms. I’m proficient in using these tools to create well logs, cross-plots, and other visualization aids to interpret data effectively. I am also familiar with open-source tools such as Python libraries (e.g., lasio) for log data processing and analysis.

For example, in Petrel, I’ve used the integrated capabilities for seismic interpretation, well log analysis and reservoir modeling to build comprehensive subsurface models. In Techlog, I frequently leverage the advanced log processing and interpretation modules for tasks like depth shifting, log calibration, and petrophysical calculations.

Missing or incomplete log data is a common challenge in log analysis. Several strategies can be employed to address this:

• Data Recovery: Attempt to recover missing data by contacting the logging company or accessing archived data. This is the preferred method whenever possible.
• Interpolation: If data recovery fails, interpolation techniques can be used to estimate missing values. Simple linear interpolation may suffice for minor gaps, while more sophisticated methods such as spline interpolation may be required for larger gaps. However, interpolation should be done cautiously and the resulting data should be appropriately flagged and treated in further analysis.
• Log Substitution: If a particular log is entirely missing, it may be possible to substitute it using a similar log from a nearby well, assuming geological similarity. This must be handled with great care as this method introduces a significant source of uncertainty.
• Statistical Methods: Statistical approaches can be employed to estimate missing values based on the correlations between different logs. This requires a robust understanding of the statistical properties of the available data.

The choice of method depends on the extent of missing data, the quality of available data, and the objectives of the analysis. It’s crucial to document all assumptions and limitations associated with data handling.

Log data plays a vital role in well completion design. Understanding reservoir properties derived from log analysis is essential for optimizing production.

• Perforation Design: Log data helps determine the optimal zones for perforating the casing. For instance, identifying hydrocarbon-bearing intervals with high permeability and low water saturation allows for targeted perforation.
• Completion Type Selection: The choice between different completion types (e.g., gravel pack, openhole completion) depends on reservoir characteristics (such as formation strength and permeability) deduced from log analysis.
• Fracture Stimulation Design: For unconventional reservoirs, log-derived data on natural fractures, stress orientation and rock mechanical properties are critical for designing effective hydraulic fracturing treatments.
• Artificial Lift System Selection: The need for artificial lift (e.g., ESP, gas lift) and the design of the chosen system are influenced by reservoir pressure and fluid properties determined using log data.

For instance, if logs indicate a low-permeability reservoir, a more extensive fracturing treatment might be required compared to a high-permeability reservoir. Ignoring log data in the completion design phase could lead to suboptimal well performance and lower hydrocarbon recovery.

In a recent project involving a carbonate reservoir, we encountered a challenging interpretation issue related to identifying the fluid contacts (oil-water and gas-oil contacts). The conventional resistivity logs were ambiguous due to complex lithology and the presence of shaly layers. These shaly layers impacted the accuracy of conventional water saturation calculations.

To overcome this, we employed a multi-faceted approach:

1. Advanced Log Analysis Techniques: We used advanced techniques like Dual Water Model and Waxman-Smits model to account for the effect of clay on resistivity measurements.
2. Core Analysis Integration: We integrated detailed core analysis data, including fluid saturation measurements from core plugs, to calibrate and validate our log interpretations. This provided ground truth data to constrain the log interpretations.
3. Nuclear Magnetic Resonance (NMR) Log Data: NMR logs provided additional information on pore size distribution and fluid typing, aiding in better differentiation between oil and water.
4. Cross-plotting and Statistical Analysis: We employed various cross-plots (e.g., porosity vs. water saturation) and statistical methods to identify correlations between different log parameters and refine our estimates.

By integrating multiple data sources and employing advanced log analysis techniques, we successfully identified the fluid contacts with increased confidence, providing more accurate reservoir characterization and resulting in improved estimations of hydrocarbon reserves.