What are the steps for determining optimal well placement with reservoir characterization?

1 Data acquisition

The first step is to acquire reliable and relevant data from the reservoir. This may include seismic surveys, well logs, core samples, fluid samples, pressure tests, and production data. The data should cover the spatial and temporal variations of the reservoir parameters, such as porosity, permeability, saturation, pressure, temperature, and flow rate. The data should also be quality checked and validated to ensure accuracy and consistency.

2 Data integration

The second step is to integrate the data from different sources and scales into a coherent and comprehensive model of the reservoir. This may involve using software tools, such as geostatistics, geocellular modeling, and reservoir simulation, to interpolate, extrapolate, and correlate the data. The model should capture the heterogeneity, anisotropy, and uncertainty of the reservoir properties and behavior. The model should also be calibrated and updated with new data as they become available.

4 Well evaluation

The fourth step is to evaluate the well based on the expected outcomes and the actual results. This may include using analytical or numerical methods, such as decline curve analysis, material balance analysis, or reservoir simulation, to estimate the well productivity, recovery, and profitability. The evaluation should also compare the well performance with the baseline or benchmark scenarios, such as other wells in the same field or similar fields. The evaluation should also identify the gaps and opportunities for improvement.

5 Well optimization

The fifth step is to optimize the well based on the evaluation and the feedback. This may include adjusting the well parameters, such as flow rate, pressure, choke size, or injection rate; modifying the well operations, such as artificial lift, stimulation, or workover; or changing the well strategy, such as infill drilling, sidetracking, or redrilling. The optimization should aim to maximize the value of the well while minimizing the cost and risk.

6 Well monitoring

The sixth step is to monitor the well based on the optimization and the changes. This may include using sensors, gauges, meters, or cameras to measure and record the well data, such as pressure, temperature, flow rate, fluid composition, or sand production. The monitoring should also use software tools, such as data analytics, machine learning, or artificial intelligence, to analyze and interpret the well data, such as trends, patterns, anomalies, or alerts. The monitoring should also enable real-time or near-real-time decision making and control.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?