Discovered Petroleum Initially in Place

Test Your Knowledge

Quiz: Unlocking the Potential: Discovering Petroleum Initially in Place (PIIP)

Instructions: Choose the best answer for each question.

1. What does PIIP stand for?

a) Petroleum Initially in Place b) Potential Initially in Place c) Production Initially in Place d) Projected Initially in Place

2. Which of the following is NOT a key element of PIIP?

a) Total estimated volume of oil or gas in a reservoir b) Current market price of oil or gas c) Oil and gas already extracted d) Remaining reserves yet to be tapped

3. What is the key difference between Commercial PIIP and Sub-commercial PIIP?

a) Commercial PIIP is extracted using advanced technology, while Sub-commercial PIIP uses traditional methods. b) Commercial PIIP is located in onshore reservoirs, while Sub-commercial PIIP is found offshore. c) Commercial PIIP is economically viable to extract, while Sub-commercial PIIP is not. d) Commercial PIIP is used for domestic consumption, while Sub-commercial PIIP is exported.

4. Which of the following is NOT a benefit of understanding PIIP?

a) Resource Assessment b) Production Planning c) Determining the best drilling technique d) Market Analysis

5. How does PIIP change over time?

a) It remains constant throughout the life of a reservoir. b) It increases as new reserves are discovered. c) It decreases as oil or gas is extracted. d) It fluctuates based on market demand.

Exercise:

Scenario: An oil company has discovered a new oil field. They have estimated the PIIP to be 100 million barrels. Based on current technology and market conditions, they believe 60% of the oil can be extracted economically.

Task: Calculate the following:

• Commercial PIIP: How many barrels of oil are considered commercially viable?
• Sub-commercial PIIP: How many barrels of oil are considered sub-commercial?

Techniques

Chapter 1: Techniques for Estimating Discovered Petroleum Initially in Place (PIIP)

Estimating PIIP relies on a combination of geological, geophysical, and engineering data. Several techniques are employed, each with its strengths and limitations:

1. Volumetric Method: This is the most common technique, estimating PIIP by calculating the hydrocarbon pore volume within a reservoir. It involves:

• Defining reservoir geometry: Determining the area and thickness of the reservoir using seismic data, well logs, and geological interpretations. This often involves creating a 3D geological model.
• Estimating porosity and hydrocarbon saturation: Well logs provide crucial data on porosity (the percentage of pore space in the rock) and hydrocarbon saturation (the percentage of pore space filled with hydrocarbons). These values are then interpolated across the reservoir.
• Calculating bulk volume: Multiplying the reservoir area and thickness gives the bulk volume of the reservoir rock.
• Calculating hydrocarbon pore volume: This is obtained by multiplying the bulk volume by porosity and hydrocarbon saturation.
• Converting to PIIP: Finally, the hydrocarbon pore volume is converted to PIIP using the formation volume factor (FVF), which accounts for the expansion of hydrocarbons as they move from reservoir conditions to standard conditions.

2. Material Balance Method: This technique uses pressure and production data from a reservoir to estimate PIIP. It relies on the principle of mass conservation, assuming that the total amount of hydrocarbons in the reservoir remains constant (excluding any water influx or gas cap expansion). The accuracy depends on the quality and completeness of the production history data.

3. Decline Curve Analysis: This method uses historical production data to extrapolate future production and estimate the original oil or gas in place. It's primarily useful for mature fields with established production history. Various decline curve models (exponential, hyperbolic, etc.) can be applied depending on the reservoir characteristics.

4. Analogue Studies: This involves comparing the reservoir under consideration with similar reservoirs that have been extensively studied. The PIIP of the analogue reservoir can be used as a basis for estimating the PIIP of the target reservoir, adjusting for differences in size, properties, and production history. This approach relies heavily on the selection of truly analogous reservoirs.

Limitations: All techniques have inherent uncertainties, stemming from the incomplete nature of subsurface data and the complex nature of reservoir behavior. Uncertainty analysis is crucial to assess the reliability of PIIP estimates.

Chapter 2: Models Used in PIIP Estimation

Accurate PIIP estimation relies heavily on building robust geological and reservoir models. These models integrate various data sources to create a three-dimensional representation of the reservoir. Key models include:

1. Geological Models: These models depict the spatial distribution of reservoir rock properties, including lithology (rock type), porosity, permeability, and fluid saturation. They are built using geological interpretations of seismic data, well logs, and core analysis. Common software packages like Petrel, RMS, and Kingdom are used to create these models.

2. Reservoir Simulation Models: These are numerical models that simulate the flow of fluids within the reservoir over time. They are used to predict future production performance and to optimize production strategies. They require detailed input data, including reservoir geometry, rock properties, fluid properties, and well configurations. Sophisticated reservoir simulators like CMG, Eclipse, and INTERSECT are commonly used.

3. Geostatistical Models: These models use statistical techniques to interpolate reservoir properties between well locations. Kriging and sequential Gaussian simulation are common geostatistical methods used to create realistic representations of reservoir heterogeneity.

4. Static Models: These models represent the reservoir at a specific point in time, capturing the initial reservoir conditions before production begins. They are essential for estimating PIIP.

5. Dynamic Models: These models simulate the changes in reservoir conditions over time, incorporating the effects of production. They are used to forecast future production and to optimize production strategies.

The choice of model depends on the availability of data, the complexity of the reservoir, and the objectives of the study. Often, a combination of models is used to obtain the most accurate PIIP estimate.

Chapter 3: Software for PIIP Estimation

Several software packages are used in the industry for PIIP estimation. These packages provide tools for data analysis, geological modeling, reservoir simulation, and uncertainty analysis. Some prominent examples include:

• Petrel (Schlumberger): A comprehensive suite of tools for geological modeling, reservoir simulation, and production forecasting. It integrates various data sources and allows for the creation of detailed 3D reservoir models.

• RMS (Roxar/Emerson): Another powerful software package offering similar functionalities to Petrel, with strong capabilities in geostatistical modeling and uncertainty analysis.

• Kingdom (IHS Markit): Provides a suite of tools for seismic interpretation, geological modeling, and reservoir characterization.

• CMG (Computer Modelling Group): A leading reservoir simulation software package used for dynamic modeling and production forecasting.

• Eclipse (Schlumberger): Another widely used reservoir simulator offering advanced capabilities for complex reservoir simulations.

• INTERSECT (Roxar/Emerson): A powerful reservoir simulation tool known for its ability to handle large and complex reservoir models.

These software packages are often integrated with other specialized tools for data processing, visualization, and reporting. The choice of software depends on the specific needs of the project and the experience of the users.

Chapter 4: Best Practices in PIIP Estimation

Accurate and reliable PIIP estimation requires adherence to best practices throughout the entire process:

• Data Quality Control: Ensuring the accuracy and consistency of all input data is crucial. This involves thorough data validation, quality checks, and error correction.

• Comprehensive Data Integration: Integrating all available data sources (seismic data, well logs, core data, production data) is essential for building a comprehensive understanding of the reservoir.

• Robust Geological Modeling: Creating realistic and geologically sound geological models is paramount. This requires expertise in geology, geophysics, and reservoir engineering.

• Appropriate Model Selection: Choosing the appropriate models and techniques based on the characteristics of the reservoir and the available data is crucial.

• Uncertainty Analysis: Quantifying the uncertainty associated with PIIP estimates is essential for making informed decisions. Monte Carlo simulation is commonly used for this purpose.

• Peer Review: Subjecting PIIP estimates to peer review by independent experts helps to identify potential biases and errors.

• Transparency and Documentation: Maintaining detailed records of the methods used, assumptions made, and uncertainties quantified ensures transparency and reproducibility of the results.

• Regular Updates: PIIP estimates should be regularly updated as new data becomes available and as understanding of the reservoir improves.

Following these best practices enhances the reliability and value of PIIP estimates, supporting more informed decision-making in exploration and production.

Chapter 5: Case Studies in PIIP Estimation

Case studies showcase the application of PIIP estimation techniques in various geological settings and reservoir types. These examples highlight the challenges and successes encountered in real-world projects:

(Note: Specific case studies would require detailed information on actual oil and gas fields, which is often proprietary and confidential. Instead, I'll outline the types of case studies that would be included in a comprehensive chapter.)

• Case Study 1: A conventional reservoir in a clastic sedimentary basin: This could detail the application of the volumetric method, using seismic data and well logs to estimate PIIP. Challenges related to reservoir heterogeneity and uncertainty quantification would be discussed.

• Case Study 2: A carbonate reservoir with complex fracture systems: This case study would illustrate the challenges of modeling fractured reservoirs and the use of specialized techniques to estimate PIIP. The limitations of the volumetric method in such settings would be highlighted.

• Case Study 3: A heavy oil reservoir with high viscosity: This case study would focus on the challenges of producing heavy oil and the impact of viscosity on PIIP estimation and recovery factor. The use of specialized reservoir simulation models would be described.

• Case Study 4: A gas condensate reservoir with a volatile oil component: This case study would illustrate the complexities of modeling multiphase flow in gas condensate reservoirs and the challenges of accurately predicting PIIP.

• Case Study 5: A case study highlighting the impact of improved seismic imaging techniques on PIIP estimation accuracy: This case study would show how advancements in technology lead to better subsurface characterization and improved PIIP estimates.

Each case study would include a description of the reservoir, the methods used for PIIP estimation, the results obtained, and a discussion of the uncertainties and limitations. These case studies would provide valuable insights into the practical applications of PIIP estimation and the challenges involved.