Identification and Comparison of True Net Fracturing Pressures Generated by Pumping Fluids with Different Rheology into the Same Formations

The instantaneous shut-in pressure (ISIP) serves as an indication of the excess pressure in the hydraulic fracture due to the effect of fluid viscosity and pressure required to break the formation at the fracture tip. The ISIP value will be close to or at the fracture propagation pressure and will be greater than the fracture pressure. The ISIP is often estimated to be the pressure after the pumps are shut down, and the beginning of a pressure decline. Many approaches have been developed to estimate the ISIP from the falloff data. The development of these approaches is attributed to the persistent trials due to the difficulty of quantifying the ISIP value accurately. Giving bottomhole pressures, ISIP can be estimated by subtracting the friction pressure drop from bottomhole pressure. This approach tends to overestimate the value of ISIP as it doesn't account for friction near the wellbore or through the perforations. Another common approach to estimate ISIP is by drawing a straight line on the early falloff portion of the Diagnostic Fracture Injection Tests (DFIT). Previous studies show that the choice of ISIP affects the net pressure calculations, but not the slope of the derivative curves and the flow regime identification. This paper presents field cases where the values of ISIP affects the interpretation of the reservoir characteristics. Thus, the determination of accurate ISIP is very crucial. This paper reviews the previously proposed approaches for determining the ISIP and provide a state of the art simple method to determine ISIP from non-ideal falloff data. The ISIP determined from the proposed method is verified by examination of the semi-log derivative plot, and the interpreted reservoir characteristics were found to be consistent with both field and lab observations. The method was validated using field DFITs falloff data from high-pressure dependent leakoff formations as well as formations that yield normal leakoff pressure dependent. The novelty of the proposed method is in the simplicity of determination of ISIP and the consistency with the field observations. A number of field examples from the Barnett shale are illustrated using mechanisms previously proposed in the literature as well as the method presented in this paper. The later provided consistent ISIP values after multiple iterations. Subsequently, the reservoir characteristics and calculated parameters were uniform within the same pad of wells.

Determination of ISIP of Non-Ideal Behavior During Diagnostic Fracture Injection Tests

Ibrahim

et al. 2019

Automated Procedure for Quantifying ISIP and Friction Losses from Stage by Stage Hydraulic Fracture Treatment Falloff Data

Alwarda

Liu

Nikolaou

et al. 2020

While diagnostic fracture injection tests (DFIT) data is relatively rare, most hydraulic fracture treatment stages in multiple transverse fracture horizontal wells (MTFHWs) follow pressure and rate data during pumping with several minutes of pressure falloff data after the end of pumping. Recent papers have shown value in applying analysis developed for DFIT data to hydraulic fracture treatment falloff (HFTF) data. As is often the case with routinely acquired operational data, the time required for analyzing HFTF datasets for each of the treatment stages in a long horizontal well may be prohibitive. This paper offers an automated analysis procedure that starts from standard treatment pressure and rate data and produces estimates for wellbore and perforation friction loss, near wellbore tortuosity friction loss, and the instantaneous shut in pressure (ISIP). Steps in the automated procedure include isolating the HFTF data from the rest of the hydraulic fracture treatment data (which is typically subject to hydraulic hammer), applying an optimized low-pass filter (LPF), and computing the friction and ISIP estimates by automating a previously published graphic procedure. We employ the automated analysis on field data that was previously analyzed by hand. Then we compare and contrast between the two analyses. The comparison between manual and automatically analyzed fracture treatment falloffs demonstrates that the automated procedure reproduces the previous analyses except in a few cases that pose special challenges to any analysis. The new field dataset results demonstrate the approach is practical for field application. Variations in friction loss and ISIP estimates along MTFHWs provide data useful for well and pad completion design decisions related to perforating strategies, and fracture and well spacing.

Machine Learning Approach to Quantifying ISIP and Friction Losses from Stage by Stage Hydraulic Fracture Treatment Falloff Data

Alwarda

2020

While diagnostic fracture injection tests (DFIT) data is relatively rare, pressure and flow rate data are widely collected for hydraulic fracture treatment stages in multiple transverse-fracture horizontal wells, both during pumping and during pressure falloff after the end of pumping. Recent papers have shown value in applying analysis developed for DFIT data to hydraulic fracture treatment falloff (HFTF) data. As is often the case with routine processing of large volumes of data, the time required for analyzing HFTF datasets for each of the treatment stages in a long horizontal well may be overly expensive in time and human resources. To address this issue, this paper applies machine learning techniques to standard HFTF pressure data, to produce data-driven mathematical models that estimate wellbore and perforation friction loss, near-wellbore tortuosity friction loss, and the instantaneous shut-in pressure (ISIP). The models are trained based on a small subset of hydraulic fracture treatment stages, data from for which is processed manually, to provide targets for model outputs. Model structure is based on multivariate statistical methods, including principal component regression (PCR) and partial least squares regression (PLSR). Comparison between state-by-stage friction losses and ISIP values estimated manually and those quantified by machine learning reveals good agreement and underscores the value of the machine learning approach as a practical tool for field application tasks, including well and pad completion design decisions related to perforating strategies, and fracture and well spacing.