Integrating Rock Properties and Fracture Treatment Data to Optimize Completions Design
- Eric Bruesewitz (Hawkwood Energy) | Jessica Iriarte (Well Data Labs) | Joel Mazza (FractureID) | Carrie Glaser (FractureID) | Eric Marshall (FractureID) | Scott Brooks (Hawkwood Energy)
- Document ID
- Society of Petroleum Engineers
- SPE Liquids-Rich Basins Conference - North America, 5-6 September, Midland, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 4.1 Processing Systems and Design, 1.2.3 Rock properties, 3 Production and Well Operations, 2.5.2 Fracturing Materials (Fluids, Proppant), 1.6 Drilling Operations, 2 Well completion, 4 Facilities Design, Construction and Operation, 0.2.2 Geomechanics, 0.2 Wellbore Design, 1.6.6 Directional Drilling, 2.4 Hydraulic Fracturing, 4.1.2 Separation and Treating
- Geomechanics, Data Integration, Fracture Treatment Data, Completions Optimization
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- 250 since 2007
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A horizontal well landed in a single formation rarely encounters homogeneous rock from the heel to the toe of the wellbore. When analyzing treatment responses that occur during hydraulic fracturing, a decreasing trend in surface treating pressure in sequential stages is typically attributed to reduced friction within the casing or frac string. However, there are several variances in treating pressure that are not readily explained by examining the surface pressures and pipe friction in isolation. These variances are also apparent when looking at bottom hole injectivity. Combining surface data and geomechanical data quickly reveals the degree of variability in rock properties along a lateral and the impact that variability can have on a completion, leading to a more optimal design. This paper demonstrates how engineers can take advantage of their most detailed completions and geomechanical data by looking for trends arising from past detailed treatment analyses and applying that gained knowledge to future completions.
This study relies on the analysis of proprietary high-resolution geomechanical data derived from the processing of accelerations measured at the drillbit and high-frequency fracture treatment data recorded at one-second intervals. The data were standardized to a common format, screened for quality control, normalized, and analyzed using a data management platform. The methodology combines critical mechanical rock properties such as Young's Modulus, and Poisson's ratio with high-frequency fracture treatment data, including treating pressures, rates, and fluid and proppant volumes. Further application of the geomechanical data to derive brittleness allows for construction of a more predictive petromechanical model to optimize completion approaches.
A brief analysis of past completions indicated virtually no correlation between gamma ray measurements along the stage and fracture treating conditions. However, when evaluating high-resolution mechanical rock properties along the lateral, a much more useful correlation exists between minimum horizontal stress variations (calculated from Poisson's Ratio) and eventual treating pressure and proppant placement difficulties. Calculated brittleness and bottomhole injectivity (which accounts for changes in slurry rate and pipe friction) also show a relationship, especially when cluster efficiency factors are included. This study of six Eagle Ford wells suggests that rock properties are the dominant variables affecting fracture treatment pressure and bottomhole injectivity. This method can be used to predict trouble stages, improve operational efficiencies, and optimize proppant placement.
This paper proposes a process to improve completion efficiency while demonstrating the value of information contained in high-resolution and high-frequency datasets. Historically underutilized, these datasets are playing an increasingly prevalent role in advanced analytics due to improved and novel technologies for data management and interpretation. This process is useful to ask better questions and to improve critical decision making with real data.
|File Size||1 MB||Number of Pages||9|
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