Pulsed Neutron Log

Test Your Knowledge

Pulsed Neutron Logging Quiz

Instructions: Choose the best answer for each question.

1. What is the primary purpose of Pulsed Neutron Logging (PNL)?

a) To measure the density of the formation. b) To identify and quantify hydrocarbons behind casing. c) To determine the porosity of the formation. d) To measure the electrical conductivity of the formation.

2. How does PNL distinguish between water and hydrocarbons?

a) By measuring the amount of gamma rays emitted by the formation. b) By measuring the amount of neutrons captured by the formation. c) By measuring the amount of hydrogen atoms present in the formation. d) By measuring the amount of carbon atoms present in the formation.

3. What is the main advantage of using PNL in cased wells?

a) It can be used to measure the pressure in the formation. b) It can be used to determine the temperature of the formation. c) It can be used to evaluate formations after completion. d) It can be used to measure the permeability of the formation.

4. What does a high Hydrogen Index (HI) value typically indicate?

a) Presence of hydrocarbons. b) Presence of water. c) Presence of gas. d) Presence of shale.

5. Which of the following is NOT a typical application of PNL in oil and gas operations?

a) Reservoir evaluation. b) Production optimization. c) Well completion design. d) Measuring the amount of drilling mud used.

Pulsed Neutron Logging Exercise

Scenario:

An oil and gas company is evaluating a newly drilled well. The PNL log shows a high HI value in the upper part of the formation, and a low HI value in the lower part of the formation.

Task:

Explain what this data indicates about the formation. What are the potential implications for well production?

Techniques

Pulsed Neutron Log: A Detailed Exploration

This document expands on the capabilities of Pulsed Neutron Logs (PNL), breaking down the subject into key areas.

Chapter 1: Techniques

Pulsed Neutron Logging (PNL) employs a pulsed neutron source, typically a high-energy accelerator generating 14 MeV neutrons, to interrogate the formation behind casing. These neutrons are emitted in short bursts, allowing for separation of prompt and delayed gamma rays. The process involves several key steps:

1. Neutron Emission: The tool emits a short burst of fast neutrons.
2. Neutron Moderation and Capture: Fast neutrons collide with atomic nuclei in the formation, losing energy (moderating) through elastic scattering. Hydrogen atoms, due to their low mass, are particularly effective at moderating neutrons. Once slowed down to thermal energies, neutrons are captured by atomic nuclei, primarily chlorine and hydrogen.
3. Gamma Ray Emission: Neutron capture by these nuclei results in the emission of characteristic gamma rays. Hydrogen capture produces a prompt 2.2 MeV gamma ray, while chlorine and other elements produce gamma rays with different energies and decay times.
4. Gamma Ray Detection: The tool's detectors measure the intensity and timing of these gamma rays. The count rate of gamma rays is recorded as a function of time since the neutron pulse.
5. Data Acquisition and Processing: The detected gamma ray counts are recorded and processed to generate various logs, including the hydrogen index (HI), porosity, and potentially lithology information. The time-dependent nature of the gamma ray signal allows for separation of information from different depths of investigation.

Different PNL techniques exist, varying in the type of neutron source, detector configuration, and data processing methods. These variations allow for optimization for specific formation conditions and objectives. For example, the use of different energy windows can isolate specific gamma ray emissions, enhancing the discrimination between various elements. The spacing between the neutron source and detectors also affects the depth of investigation.

Chapter 2: Models

The interpretation of PNL data relies on mathematical models that describe the interaction of neutrons with the formation. These models are essential for converting the measured gamma ray counts into formation properties such as porosity, water saturation, and lithology. Key models include:

• Diffusion-Equation Models: These models describe the transport of neutrons in the formation using the diffusion equation. They are relatively simple but may not accurately represent the complex interaction processes in heterogeneous formations.
• Monte Carlo Simulations: Monte Carlo simulations use statistical methods to track the individual paths of neutrons as they interact with the formation. These simulations provide a more accurate representation of neutron transport but require significant computational resources.
• Analytical Models: Simpler analytical models are used for quick estimations, often incorporating empirical relationships derived from experimental data or more sophisticated simulations. These offer a faster but potentially less accurate alternative.

The choice of model depends on the complexity of the formation and the desired accuracy of the results. In practice, a combination of models and empirical corrections may be used to improve the accuracy of the interpretation. Calibration and validation of the models using well-known formations or core data is crucial for reliable results.

Chapter 3: Software

Specialized software packages are used to process and interpret PNL data. These packages typically include features for:

• Data Acquisition and Quality Control: Verification of data integrity and correction of any artifacts or noise.
• Log Display and Analysis: Visualization of the PNL logs in conjunction with other well logs.
• Model-Based Inversion: Use of mathematical models to invert the measured data and obtain formation properties.
• Reservoir Simulation Integration: Integration of PNL data into reservoir simulation models for improved reservoir characterization and production forecasting.
• Report Generation: Automated generation of reports summarizing the interpretation results.

Examples of software packages include those offered by Schlumberger, Halliburton, and Baker Hughes. These commercial packages often incorporate proprietary algorithms and models developed over many years of experience. Open-source tools are also available, offering greater flexibility but potentially requiring more expertise for use.

Chapter 4: Best Practices

Optimizing the use of PNL and maximizing data quality requires adherence to best practices:

• Careful Tool Selection: Choosing the appropriate tool based on the specific wellbore and formation conditions.
• Thorough Pre-Job Planning: Defining the objectives, identifying potential challenges, and selecting appropriate logging parameters.
• Quality Control of Logging Operations: Ensuring that the logging run is conducted according to established procedures and that the acquired data are of high quality.
• Appropriate Data Processing Techniques: Applying proper correction methods for environmental effects (e.g., borehole size, casing effects).
• Integrated Interpretation: Analyzing PNL data in conjunction with other well logs (e.g., gamma ray, density, neutron porosity) for a more comprehensive understanding of the formation.
• Calibration and Validation: Regularly calibrating and validating the interpretation models against core data or known formations.
• Uncertainty Assessment: Quantifying the uncertainty associated with the interpreted formation properties.

Chapter 5: Case Studies

Several case studies illustrate the successful application of PNL in various scenarios:

• Case Study 1: Improved Water/Hydrocarbon Differentiation in a Cased Well: PNL successfully identified bypassed hydrocarbons in a previously water-saturated zone behind casing, leading to an increase in production. The detailed time-dependent analysis was crucial for discriminating between the different formations.

• Case Study 2: Monitoring Enhanced Oil Recovery (EOR): PNL was used to monitor the movement of injected fluids during a CO2-EOR project, allowing for optimization of the injection strategy and improvement of the overall recovery efficiency. Changes in hydrogen index over time provided direct insight into the efficacy of the injection process.

• Case Study 3: Reservoir Characterization in a Complex Formation: PNL provided key information regarding porosity and lithology in a complex carbonate reservoir, enabling a more accurate reservoir model and improved production forecasting. The integration of PNL data with other well logs was crucial for obtaining a reliable geological interpretation.

These case studies demonstrate the versatility and power of PNL in enhancing reservoir understanding, optimizing production, and improving overall operational efficiency in the oil and gas industry. Further detailed case studies are frequently published in industry journals and conferences.