A Data-Driven Water-Soaking Model for Optimizing Shut-In Time of Shale Gas/Oil Wells Prior to Flowback of Fracturing Fluids

Abstract Frac-driven interactions (FDIs) often lead to sharp decline in gas and oil production rates of wells in shale gas/oil reservoirs. How to minimize the FDI is an open problem in the oil and gas industry. Xiao et al.’s (2019, “An Analytical Model for Describing Sequential Initiation and Simultaneous Propagation of Multiple Fractures in Hydraulic Fracturing Shale Oil/Gas Formations,” Energy Sci Eng., 7(5), pp. 1514–1526.) analytical model for two-fracture systems was extended in this study to obtain a general model for handling multiple fractures. The general model was used to identify engineering factors affecting the maximum permissible stage fluid injection time for minimizing FDI. On the basis of model results obtained, we found that increasing fluid injection rate can create more short fractures and thus increase the maximum permissible stage injection time before FDI occurs. Use of dilatant type of fracturing fluid (n > 1) can reduce the growth of long fractures, promote the creation of more short fractures, and thus increase the maximum permissible stage injection time before FDI occurs. It is also expected that injecting dilatant type of fracturing fluid at high rate will allow for longer injection time and thus larger injection volume, resulting in larger stimulated reservoir volume (SRV) with higher fracture intensity and thus higher well productivity and hydrocarbon recovery factor.

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Lab-Supported Hypothesis and Mathematical Modeling of Crack Development in the Fluid-Soaking Process of Multi-Fractured Horizontal Wells in Shale Gas Reservoirs

Energies ◽

10.3390/en13051035 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1035

Author(s):

Zhiyong Huang ◽

Boyun Guo ◽

Rashid Shaibu

Keyword(s):

Crack Propagation ◽

Shale Gas ◽

Oil Wells ◽

Pressure Data ◽

Fracturing Fluid ◽

Well Productivity ◽

Soaking Time ◽

Core Samples ◽

Inflow Performance ◽

Optimum Water

The objective of this study is to develop a technique to identify the optimum water-soaking time for maximizing productivity of shale gas and oil wells. Based on the lab observation of cracks formed in shale core samples under simulated water-soaking conditions, shale cracking was found to dominate the water-soaking process in multi-fractured gas/oil wells. An analytical model was derived from the principle of capillary-viscous force balance to describe the dynamic process of crack propagation in shale gas formations during water-soaking. Result of model analysis shows that the formation of cracks contributes to improving well inflow performance, while the cracks also draw fracturing fluid from the hydraulic fractures and reduce fracture width, and consequently lower well inflow performance. The tradeoff between the crack development and fracture closure allows for an optimum water-soaking time, which will maximize well productivity. Reducing viscosity of fracturing fluid will speed up the optimum water-soaking time, while lowering the water-shale interfacial tension will delay the optimum water-soaking time. It is recommended that real-time shut-in pressure data are measured and shale core samples are tested to predict the density of cracks under fluid-soaking conditions before using the crack propagation model. This work provides a shut-in pressure data-driven method for water-soaking time optimization in shale gas wells for maximizing well productivity and gas recovery factor.

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Water cut/salt content forecasting in oil wells using a novel data-driven approach

Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles ◽

10.2516/ogst/2019040 ◽

2019 ◽

Vol 74 ◽

pp. 68

Author(s):

Rouhollah Ahmadi ◽

Jamal Shahrabi ◽

Babak Aminshahidy

Keyword(s):

Classification Tree ◽

Salt Content ◽

Oil Wells ◽

Data Driven ◽

Var Model ◽

Support Vector ◽

Water Production ◽

Linear Discriminant ◽

Data Driven Approach ◽

Water Cut

Water cut is an important parameter in reservoir management and surveillance. Unlike traditional approaches, including numerical simulation and analytical techniques, which were developed for predicting water production in oil wells based on some assumptions and limitations, a new data-driven approach is proposed for forecasting water cut in two different types of oil wells in this article. First, a classification approach is presented for water cut prediction in sweet oil wells with discontinuous salt production patterns. Different classification algorithms including Support Vector Machine (SVM), Classification Tree (CT), Random Forest (RF), Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA) and Naïve Bayes (NB) are investigated in this regard. According to the results of a case study on a real Iranian sweet oil well, RF, CT, MLP and SVM can provide the best performance measures, respectively. Next, a Vector Autoregressive (VAR) model is proposed for forecasting water cut in salty oil wells with continuous water production during the life of the well. The proposed VAR model is verified using data of two real salty oil wells. The results confirm that the well-tuned proposed VAR model could provide reliable and acceptable results with very good accuracy in forecasting water production for the near future days.

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