Interview Questions for Well Testing and Production Logging

Well testing encompasses various techniques to evaluate reservoir properties and well performance. Different tests are selected based on the specific objectives. Here are some key types:

• Pressure Buildup Tests (PBU): Performed after a period of production to analyze reservoir pressure and permeability. Think of it like letting a squeezed sponge slowly reabsorb water – the rate tells us about the sponge’s (reservoir’s) properties.
• Pressure Drawdown Tests (PDD): Involve producing a well at a constant rate to observe the pressure decline. This helps assess reservoir deliverability and identify potential wellbore issues. Imagine draining a water tank – the rate at which the water level drops indicates the tank’s (reservoir’s) capacity.
• Injection Tests: Involve injecting fluids into the well to determine reservoir injectivity and assess potential for water or gas injection projects. Similar to filling the water tank – how quickly it fills tells us about its capacity to accept fluid.
• Multiple-Rate Tests: Involve changing production rates during a test to get a more comprehensive understanding of reservoir behavior. This is like repeatedly squeezing the sponge with varying force to see how the water absorption changes.
• Interference Tests: Used to determine reservoir connectivity by observing the pressure response in one well due to production or injection in a nearby well. Like having two connected sponges; squeezing one affects the water level in the other.

The choice of test depends on factors like the well’s production history, reservoir characteristics, and the specific information needed. For example, a PBU test is ideal for assessing reservoir permeability in a newly completed well, while a PDD test might be better suited for evaluating the productivity of an existing well.

Pressure buildup testing is a crucial well test used to determine reservoir properties after a period of production. The process involves:

1. Shutting-in the well: Production is stopped completely, allowing the reservoir pressure to recover.
2. Monitoring pressure: Pressure is measured at regular intervals using pressure gauges. The pressure data is recorded as a function of time (shut-in time).
3. Data analysis: The pressure buildup data is analyzed using specialized software and techniques, such as Horner’s method or a type curve matching, to determine reservoir parameters. These parameters include permeability, skin factor, and reservoir pressure.

Imagine you’re squeezing a sponge (reservoir) and then releasing it. The pressure buildup test measures how quickly the sponge recovers its original shape, indicating how easily water (fluid) flows through its pores (permeability).

Production logging involves running tools downhole while the well is producing to measure various parameters. Key measurements include:

• Pressure: Pressure profiles along the wellbore provide insights into pressure gradients and potential flow restrictions.
• Temperature: Temperature profiles help identify fluid movement and heat transfer in the well. Unusual temperature gradients can signal fluid entry points or changes in reservoir properties.
• Flow Rate: Flow rate profiles show the fluid distribution in the wellbore, revealing zones of high or low production.
• Fluid Composition: Specialized tools can measure the composition (gas, oil, water) of the produced fluids at different depths. This helps identify fluid contacts and determine the productivity of each zone.
• Hold-up: This measurement indicates the amount of liquid in the wellbore, which is crucial for estimating the amount of liquid that would be held up in the tubing under different flow regimes.

These measurements provide a detailed picture of well performance and fluid flow dynamics, helping identify production bottlenecks and optimize well production.

Interpreting a pressure drawdown test involves analyzing the pressure decline during a period of constant production. The data is typically plotted on a semi-log graph of pressure drawdown versus time. The slope of the straight-line portion of the graph is used to determine the reservoir permeability, while the intercept helps calculate the skin factor. A steeper slope indicates higher permeability. The analysis also considers the wellbore storage effect which influences early time data. Type curves matching against theoretical models is also frequently used for interpretation. This helps to identify reservoir boundaries and other crucial features. It’s like watching the water level drop in a tank – the rate of the drop tells us about the reservoir’s properties and potential for production.

Analysis includes accounting for wellbore storage and skin effects. These can significantly impact the early-time behavior of the pressure during a drawdown test. Proper accounting and analysis of these effects is vital for accurate determination of reservoir permeability and other relevant properties.

Well testing, while powerful, has limitations:

• Reservoir heterogeneity: Well tests assume a homogeneous reservoir, but real reservoirs are often complex and heterogeneous. This can lead to inaccurate interpretations.
• Wellbore effects: Factors such as wellbore storage, skin effect, and non-Darcy flow can affect the pressure response, making interpretation more challenging.
• Data quality: Inaccurate pressure measurements or incomplete data can lead to errors in the interpretation.
• Time and cost: Well testing can be time-consuming and expensive, especially for complex tests.
• Limited vertical resolution: Many well tests provide limited information about the vertical heterogeneity of the reservoir.

Understanding these limitations is crucial for proper test design and interpretation, and for selecting complementary techniques such as production logging for a more comprehensive reservoir characterization.

The skin effect in well testing refers to the alteration of reservoir pressure near the wellbore due to factors such as partial penetration, formation damage, or wellbore stimulation. A positive skin factor indicates a reduced flow capacity near the wellbore (damage), while a negative skin factor indicates increased flow capacity (stimulation).

Imagine a straw in a glass of water. A damaged wellbore is like a partially clogged straw, restricting the flow of water. Stimulation is like widening the straw, allowing for easier and faster water flow. The skin factor quantifies this impact on flow.

The skin factor is a dimensionless quantity that accounts for this near-wellbore alteration of flow. It’s crucial to understand the skin effect because it can significantly affect productivity and injectivity indices and must be accounted for during pressure transient analysis.

Identifying and mitigating measurement errors in well testing is critical for accurate interpretation. Strategies include:

• Calibration and maintenance of equipment: Regular calibration of pressure gauges and other measurement tools is essential to ensure accuracy.
• Proper well preparation: Ensure the well is properly shut-in or produced at a stable rate during the test to avoid spurious data.
• Data validation: Check for inconsistencies or outliers in the data and investigate their potential causes.
• Using multiple sensors: Employing multiple sensors reduces the risk of errors and can help identify faulty equipment.
• Employing advanced data analysis techniques: Advanced methods can help identify and correct for systematic errors and can improve the reliability of the estimates.

A multi-faceted approach to quality control and advanced data analysis techniques is needed to minimize the impact of measurement errors and improve the accuracy of well test interpretation.

I’m proficient in several software packages used for well test analysis. These include industry-standard tools like KAPPA, MBAL, and Eclipse. KAPPA, for instance, is excellent for interpreting pressure buildup and drawdown tests, allowing for detailed analysis of reservoir properties. MBAL is particularly useful for material balance calculations, crucial for understanding reservoir fluid behavior over time. Finally, Eclipse, a reservoir simulator, can integrate well test data for history matching and forecasting reservoir performance. My experience also extends to using specialized add-ons and modules within these packages to address specific challenges in data interpretation, like dealing with multi-layered reservoirs or complex wellbore effects.

Beyond these, I’m comfortable working with data processing and analysis software like MATLAB and Python, which are invaluable for customizing analysis workflows and creating visualizations. For example, I’ve used Python to automate data cleaning and develop custom algorithms for interpreting unusual well test responses.

Production logging tools are crucial for understanding the in-situ performance of a producing well. They provide real-time measurements of various parameters downhole. Here are some key types:

• Temperature Logs: Measure the temperature profile in the wellbore. Significant temperature changes can indicate fluid flow patterns, gas channeling, or water coning.
• Pressure Logs: Measure pressure at various depths, helping to identify pressure gradients and potential flow restrictions. This data is important for understanding pressure support mechanisms in a reservoir.
• Flow Rate Logs: Directly measure fluid flow rates at different depths within the wellbore, providing insights into fluid distribution and identifying intervals with high or low production.
• Fluid Sampler/Analyzer Logs: Collect and analyze fluid samples at different depths, providing information on fluid composition, gas-oil ratio, and water cut, which are essential parameters for reservoir management.
• Gamma Ray Logs: While not strictly a production log, they’re often run in conjunction with other tools to provide a geological framework for interpreting the production data. They help correlate production behavior with specific geological formations.

These tools are often combined in a production logging suite, allowing for simultaneous measurement of multiple parameters, providing a comprehensive understanding of the well’s behavior. For example, a combined temperature and flow rate log can help pinpoint the exact location of a gas-water contact.

Interpreting production logs to identify flow regimes requires a careful analysis of the data, considering multiple parameters. The key is to look for patterns and correlations between different measurements. For example:

• Homogeneous Flow: A relatively constant flow rate and pressure gradient across the producing interval suggests homogeneous flow. Temperature logs would show a gradual change, and there would be no significant anomalies.
• Stratified Flow: The presence of distinct layers with different flow rates and pressure gradients indicates stratified flow. This is often seen in gas-oil or oil-water systems, and temperature logs may show distinct zones of different temperatures.
• Gas Coning/Water Coning: Coning is indicated by a significant increase in gas or water production at the bottom of a well, which is reflected in changes in flow rates, fluid composition, and pressure profiles measured by production logs. Temperature logs often show a characteristic temperature anomaly due to gas or water being lighter or heavier than the surrounding fluid.
• Channeling: Localized high flow rates in specific parts of the reservoir, identified via high flow rates in specific intervals in flow rate logs, indicating channeling. Temperature logs may show localized hotter zones indicating preferential flow path.

Often, sophisticated interpretation techniques, including the use of specialized software and modeling, are required to accurately identify flow regimes from complex production logs. Visualizing the data using plots of flow rate versus depth, pressure versus depth, and temperature versus depth is essential for pattern recognition.

Well testing operations can encounter several problems, many stemming from the inherent complexity of subsurface environments. Some common issues include:

• Formation Damage: The process of drilling and testing can damage the formation, altering its permeability and affecting the test results. This could be due to filter cake build up or invasion of drilling mud.
• Wellbore Storage: The wellbore itself can act as a storage reservoir, impacting the pressure response and potentially masking the true reservoir properties. This effect is more pronounced during short-term tests.
• Skin Effect: Changes in the permeability near the wellbore due to formation damage or stimulation. Skin factors needs to be accounted for when interpreting test data.
• Non-Darcy Flow: At high flow rates, non-Darcy flow effects become significant, altering the pressure response and requiring specialized interpretation techniques.
• Equipment Malfunctions: Failure of pressure gauges, flow meters, or other testing equipment can lead to inaccurate or incomplete data. This is why redundancy and quality control are paramount.
• Difficulties in isolating test intervals: In multi-layered reservoirs, problems arise while trying to conduct a test in a single zone. Fluid communication between layers can impact the test data.

Careful planning, rigorous quality control, and the use of advanced interpretation techniques are crucial to mitigate these problems and obtain reliable well test data.

Multi-rate test analysis is used to determine reservoir properties when the well is produced at several different flow rates. This approach is particularly useful in identifying reservoir heterogeneities and improving the accuracy of reservoir parameters.

The analysis typically involves:

1. Data Acquisition: Accurately recording pressure and flow rate data at each flow rate stage.
2. Data Cleaning: Removing spurious data points and correcting for noise.
3. Wellbore Storage and Skin Correction: Accounting for wellbore storage and skin effects, which can significantly influence the early-time data.
4. Type-Curve Matching: Using type curves to match the observed pressure responses with theoretical models to determine reservoir properties like permeability, skin, and storage coefficient. Software like KAPPA facilitates this process.
5. Multi-rate Analysis Techniques: Employing specialized multi-rate analysis techniques (e.g., superposition method) to combine data from different flow rates into a single interpretation. This is often done using specialized software or scripts that account for the changes in flow rate over time.
6. Parameter Estimation: Estimating reservoir parameters (permeability, skin, storage coefficient) based on the best match between the observed and theoretical pressure responses. This is usually done using a least-squares fitting approach.

The accuracy of the results depends on the quality of the data and the appropriateness of the chosen analytical model. For instance, multi-rate testing is especially helpful for high permeability reservoirs which exhibit significant wellbore storage and transient pressure effects. Therefore, appropriate techniques are needed to remove these effects and obtain accurate results.

Material balance is a fundamental concept in reservoir simulation and is closely related to well testing. It states that the total amount of fluid in a reservoir remains constant unless there’s injection or production. This principle is expressed through an equation that relates changes in reservoir pressure and volume to the amount of fluid produced or injected. This concept involves tracking the amount of reservoir fluids throughout the production history.

In reservoir simulation, material balance calculations are used to predict future reservoir performance and help history match a reservoir simulation model to actual production data. Well testing provides crucial data that helps calibrate and validate these models. Pressure buildup tests, for instance, can help determine the initial reservoir pressure, fluid properties, and reservoir volume. These data then form constraints in the material balance equation.

Essentially, well testing data helps to establish initial conditions for material balance calculations which are then employed for reservoir simulation modeling. Therefore, close integration between well testing and reservoir simulation is essential for an accurate representation of the reservoir’s dynamic behaviour.

Reservoir permeability, a measure of how easily fluids flow through a rock, is a critical parameter determined from well test data. The most common method involves analyzing the pressure response during a drawdown or buildup test.

For instance, during a drawdown test (constant flow rate), the pressure declines as fluids are withdrawn from the reservoir. The rate of pressure decline is directly related to reservoir permeability. The analysis typically uses analytical models (e.g., the superposition method) based on Darcy’s law and considers wellbore storage effects and skin factor to precisely determine permeability. Type-curve matching techniques are often employed to match the observed pressure response to theoretical models. Specific parameters that are indicative of permeability are the slope of the pressure-time derivative and the early time pressure response.

Similarly, buildup tests (after shutting in a well) show a pressure increase, again reflecting the reservoir’s permeability. The rate at which the pressure increases is analysed using specialized software and algorithms, to determine reservoir permeability. The Horner method is used in buildup tests to estimate reservoir permeability. However, the choice of analytical model used for permeability determination depends on the reservoir geometry and heterogeneity. For instance, the radial flow model is often applicable to homogeneous and isotropic reservoirs.

In summary, analyzing pressure data from well tests using appropriate analytical models, considering factors like wellbore storage and skin, allows for the precise determination of reservoir permeability, which is critical for reservoir management decisions.

Conventional reservoir well testing, typically applied to conventional reservoirs like sandstone or carbonate formations, focuses on relatively high permeability and homogeneous rock properties. The testing procedures are often simpler and interpretations rely on established analytical models based on Darcy flow. Unconventional reservoirs, such as shale gas or tight oil formations, present a significantly different picture. Their low permeability, complex fracture networks, and heterogeneous nature necessitate more advanced testing techniques and interpretation methods. Conventional techniques may be inadequate, leading to inaccurate reservoir characterization. For example, a simple drawdown test in a conventional reservoir might provide sufficient data to estimate permeability. In an unconventional reservoir, this same test might not be conclusive due to significant wellbore storage effects and the influence of complex fracture networks. This calls for more sophisticated tests, like multi-rate tests or interference tests, and advanced numerical simulation for interpretation.

Planning and executing a well test involves several crucial steps. It begins with defining clear objectives, such as determining reservoir permeability, skin factor, or identifying reservoir boundaries. Next, we need to select an appropriate test type (drawdown, buildup, interference, etc.) based on the reservoir characteristics and testing objectives. Detailed pre-test modeling is crucial to predict test behavior and optimize test duration. This often involves building a reservoir simulation model to understand the expected pressure response. Before the test, we must ensure that the well is in a stable production state and gather baseline pressure data. During the test, continuous monitoring of pressure, flow rates, and other relevant parameters (e.g., temperatures) is vital. Accurate data acquisition is paramount. Post-test data analysis involves interpreting pressure-transient data using appropriate analytical or numerical models to estimate reservoir parameters. Finally, we need to create a comprehensive test report summarizing the findings and their implications for reservoir management.

Non-Darcy flow, characterized by deviations from Darcy’s law at higher flow rates, commonly occurs in highly permeable reservoirs or near the wellbore. This non-linear relationship between flow rate and pressure gradient complicates well test interpretation because standard analytical models assume Darcy flow. Several approaches address this. One method involves using a modified Darcy’s law that accounts for non-Darcy effects. The Forchheimer equation is often employed for this purpose. Alternatively, we can use numerical simulation incorporating the Forchheimer equation to accurately model the pressure response. Another approach involves modifying the well test analysis techniques to account for non-Darcy flow. For instance, specialized software can be used that incorporates non-Darcy models, helping to compensate for the non-linearity. In practice, careful evaluation of the test data is required to determine if non-Darcy effects are significant and to select the appropriate interpretation methods. For example, a plot of flow rate versus pressure gradient might show a clear deviation from linearity, suggesting the presence of significant non-Darcy flow.

Safety is paramount during well testing. Before the operation begins, a comprehensive safety plan must be developed and approved, encompassing risk assessments and mitigation strategies. This includes identifying potential hazards such as high pressure, toxic fluids, and equipment malfunctions. Proper training of all personnel involved is crucial, covering emergency procedures and safe operating practices. All equipment must be inspected and maintained to ensure safe operation, including pressure vessels, tubing, and control systems. Appropriate personal protective equipment (PPE), including safety glasses, gloves, and protective clothing, must be used at all times. Emergency response procedures, including communication plans and evacuation routes, must be clearly defined and regularly practiced. Environmental protection measures must also be in place to prevent spills or leaks. Continuous monitoring of well pressure and flow rates helps prevent dangerous overpressure situations. Regular communication between all personnel involved is vital to ensure a safe and efficient well test operation.

Wellbore storage, referring to the compressibility of the fluids and the formation in the wellbore, significantly impacts early-time pressure responses. Two main types exist: skin effect is a near-wellbore alteration in permeability caused by drilling, completion, or formation damage and wellbore storage which refers to the volume of fluid stored (or produced) in the wellbore during a pressure change. Accounting for these effects is critical for accurate reservoir parameter estimation. Wellbore storage is usually modeled using a storage coefficient (C_s) representing the fluid volume change in the wellbore per unit pressure change. The skin effect is quantified by a dimensionless skin factor (s). Ignoring these effects leads to inaccurate permeability estimates. To account for wellbore storage, specialized interpretation techniques are utilized, such as type curve matching using superposition of storage and reservoir responses or numerical modeling incorporating wellbore storage effects. For example, early-time pressure data affected by wellbore storage would typically be ignored, focusing instead on later time data where the reservoir dominates the pressure response.

Well test data is inherently uncertain due to various sources of error, including measurement errors, model limitations, and assumptions. Addressing this uncertainty requires a multi-pronged approach. First, rigorous quality control procedures should be implemented to minimize measurement errors. This involves careful calibration and maintenance of testing equipment. Secondly, sensitivity analysis helps evaluate the impact of uncertain input parameters on the estimated reservoir properties. Thirdly, multiple interpretation methods should be employed to provide a range of possible outcomes instead of a single point estimate. Finally, using statistical methods such as Monte Carlo simulation can provide probability distributions for reservoir parameters, reflecting the uncertainty associated with the data and model limitations. For example, if multiple interpretations yield substantially different values for reservoir permeability, this indicates significant uncertainty that should be clearly stated in the final report.

Production logging provides valuable information on fluid flow profiles, but relying solely on it to characterize a reservoir has significant limitations. Production logs only give information about the flow in the wellbore, not the reservoir itself. They cannot directly measure reservoir properties such as permeability, porosity, and saturation. While they provide insights into the vertical flow distribution, the lateral extent of reservoir flow is inferred rather than directly observed. Production logging tools cannot easily measure pressure, which is a key parameter for reservoir characterization. Thus, they are best used in conjunction with well testing and other reservoir characterization methods to provide a more comprehensive understanding of reservoir behavior. Combining production log data with well test results allows for more accurate reservoir models and improved decision-making. For example, production logging can reveal flow channeling or water coning, providing information to guide well testing design and interpretation.

Analyzing multiphase flow in production logging involves determining the individual flow rates and properties of oil, water, and gas in a producing well. Several methods are employed, each with its strengths and weaknesses:

• Flow regime identification: This is the first step, determining whether the flow is annular, stratified, slug, or dispersed. Visual inspection of production logging data (e.g., pressure, temperature, and gamma ray logs) and interpretation software are used. Recognizing the flow regime is critical for accurate interpretation of subsequent measurements.
• Multiphase flow meters: These sophisticated instruments directly measure the individual flow rates and properties of the fluids. Common types include capacitance meters, gamma ray densitometers, and optical sensors. They offer high accuracy but can be expensive and challenging to deploy in complex well conditions.
• Pressure and temperature profiles: Changes in pressure and temperature along the wellbore can indicate the presence of different phases and their distribution. This indirect method is less accurate than direct flow meters, but it provides valuable contextual information. Software analysis combines these profiles with other data to estimate flow rates.
• Tracer techniques: Injecting tracers (e.g., radioactive isotopes or fluorescent dyes) into the well allows for determining the flow pathways and fractional flow rates. While providing unique insight, this requires specialized equipment and careful planning.
• Computer-aided interpretation: Software models use the measured data (from any of the above methods) along with the well geometry and fluid properties to generate detailed descriptions of multiphase flow behavior. This often involves empirical correlations and numerical simulation.

For instance, in a highly deviated well, a combination of pressure and temperature profiles alongside a capacitance flow meter might be employed to obtain a comprehensive understanding of the multiphase flow.

Integrating well testing and production logging data significantly enhances reservoir characterization by providing a comprehensive picture of the reservoir’s dynamic behavior. Well testing, typically short-term, provides information on reservoir properties like permeability and skin factor. Production logging, on the other hand, provides long-term data on flow profiles, fluid properties, and production allocation from different zones. Integration achieves a more complete understanding.

• Calibration and validation: Well test data (e.g., pressure buildup tests) can be used to calibrate or validate the parameters of the reservoir model used to interpret production logging data. This reduces uncertainty in the flow rate estimates and reservoir property determination.
• Improved flow profile determination: Production logging reveals the flow profiles within the wellbore, which, combined with well test results, helps to identify and quantify flow barriers or preferential flow paths within the reservoir.
• Enhanced reservoir modeling: The integrated data sets can be used to improve numerical reservoir simulation models. This allows for more accurate prediction of future production, optimization of well completion, and improved reservoir management.
• Water influx determination: Well test analysis can estimate water influx, and production logging provides real-time verification, improving the accuracy of water flood management.

For example, a pressure buildup test might indicate a high skin factor suggesting a near-wellbore damage. Production logging could then confirm this by showing a restricted flow around the damaged zone.

Well interference testing is a technique used to determine the hydraulic connection and properties between different wells in a reservoir. It involves observing the pressure response in one well (the observation well) due to the production or injection in another well (the test well).

The principle is based on the fact that pressure changes in one well will propagate through the reservoir and affect the pressure in neighboring wells. Analyzing this pressure response allows us to deduce:

• Reservoir connectivity: Whether the wells are communicating hydraulically.
• Reservoir permeability and porosity: These are crucial reservoir properties influencing the pressure response.
• Reservoir boundaries: The pressure behavior can be affected by reservoir boundaries (faults or edges).
• Fracture networks: Presence of fractures significantly affects pressure propagation, which can be detected using this method.

The test often involves producing one well and observing pressure changes in others. Sophisticated analytical models are employed to interpret the data and obtain the reservoir properties. It’s a valuable technique for optimizing well placement and reservoir management, particularly in fractured reservoirs or fields with complex geometries.

A Horner plot is a graphical method used to analyze pressure buildup test data to determine reservoir properties like permeability and skin factor. It’s based on the Horner equation, which describes the pressure behavior during a buildup test following a period of production.

The plot is a graph of the pressure (on the y-axis) versus the Horner time (on the x-axis), where Horner time is a function of production time and shut-in time.

Horner time = tp + Δt / (Δt / tp)

where:

• tp = production time
• Δt = shut-in time

The Horner plot typically shows a straight-line portion at later times. Extracting the y-intercept of this line gives the original reservoir pressure (before production), and the slope of the line provides information about permeability and skin factor. Skin represents the near-wellbore damage or stimulation effects.

A positive skin indicates damage, while a negative skin indicates stimulation. The interpretation requires careful analysis of the plot, paying attention to data quality and potential deviations from the ideal straight line due to factors like reservoir boundaries or wellbore storage.

Different well testing methods each have advantages and disadvantages:

• Pressure Buildup Test (PBT):
  • Advantages: Relatively simple to conduct, provides information on permeability, skin, and reservoir pressure.
  • Disadvantages: Requires a shut-in period, not suitable for all reservoir types (e.g., low permeability).
• Drawdown Test:
  • Advantages: Can provide information on permeability and wellbore storage.
  • Disadvantages: Requires continuous production, can be challenging to interpret in complex reservoirs.
• Injection Test:
  • Advantages: Useful for assessing injectivity and identifying injection barriers.
  • Disadvantages: Requires injection of fluids, may not always provide comprehensive reservoir information.
• Interference Test:
  • Advantages: Provides information on reservoir connectivity and properties between wells.
  • Disadvantages: Requires multiple wells, can be more complex to interpret.
• Pulse Test:
  • Advantages: Useful for assessing near-wellbore properties in low permeability reservoirs.
  • Disadvantages: Requires specialized equipment, data interpretation can be sensitive.

The choice of testing method depends on the specific objectives, reservoir characteristics, and operational constraints. Often a combination of tests is used to gain a more comprehensive understanding of the reservoir.

Ensuring data quality and integrity during well testing and production logging is critical for reliable interpretation. Several measures are crucial:

• Pre-test planning: Thorough planning is paramount, including defining clear objectives, selecting appropriate equipment, and ensuring the availability of skilled personnel.
• Calibration and verification: Equipment calibration before and after the test is essential to eliminate systematic errors. Cross-checking data from multiple sources helps validate the measurements.
• Data acquisition: Employing high-quality equipment and robust data acquisition systems is vital. Regular monitoring of the equipment’s performance during testing is crucial to detect and mitigate any potential issues.
• Data processing and analysis: Data processing involves removing noise, correcting for known biases, and ensuring consistency. Using standardized procedures and validated software enhances accuracy and reproducibility.
• Quality control checks: Implementing quality control checks throughout the process is essential to identify and correct errors. This includes visual inspection of logs, performing plausibility checks, and comparing data with other sources.
• Documentation: Maintaining detailed and accurate records of all aspects of the testing process, including test design, data acquisition, processing, and interpretation, ensures transparency and traceability.

For instance, in a pressure buildup test, we would verify the gauge calibration before and after, validate the pressure stability during the shut-in period, and check for potential data drift.

During a multilateral well test in a tight gas reservoir, we encountered a significant challenge: the pressure response was significantly affected by wellbore storage effects, masking the true reservoir properties. The well had multiple branches intersecting different reservoir compartments, and the complex geometry made the interpretation extremely difficult.

We overcame this challenge by adopting a multi-faceted approach:

• Detailed pre-test modeling: We created a detailed numerical model of the wellbore and reservoir system, incorporating the complex geometry and expected flow characteristics. This helped us to anticipate the potential impact of wellbore storage.
• Advanced interpretation techniques: We employed advanced well test analysis techniques, such as type-curve matching and deconvolution methods, to account for wellbore storage and obtain a better estimate of the reservoir properties.
• Sensitivity analysis: A sensitivity analysis was performed to identify the most influential parameters in the model. This helped us to focus our efforts on obtaining accurate measurements for those critical parameters.
• Data quality control: Rigorous data quality control was maintained throughout the testing process, ensuring the reliability of the input data for the model.

By integrating these strategies, we were able to successfully extract meaningful reservoir properties from the complex pressure data. The success of this project highlighted the importance of combining advanced interpretation techniques with meticulous pre-test planning and data quality control in challenging well testing scenarios.