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Predrill Pore Pressure Estimation in Shale Gas Reservoirs Using Seismic Genetic Inversion with an Example from the Barnett Shale

Sid-Ali Ouadfeul1, Mohamed Zinelabidine Doghmane2, and Leila Aliouane3

1 Geophysics, Geology and Reservoir Engineering Department, Algerian Petroleum Institute, Sonatrach, Boumerdes, Algeria

2 Department of Geophysics, FSTGAT, University of Science and Technology Houari Boumediene, Algiers, Algeria

3 LABOPHYT, Faculty of Hydrocarbons and Chemistry, University M'hamed Bougara of Boumerdes, Boumerdes, Algeria

KEYPOINTS

• Artificial neural network used to estimate pore pressure prior to drilling using seismic genetic inversion.
• Stacked 3D seismic amplitudes near three wells used as input for the multilayer perceptron neural network.
• The weights of connections between neurons are optimized during training, and the entire seismic cube is transmitted through the neural network.
• Linear relationship between depth and velocity produced using sonic well-log data of a vertical well, used as a vertical trend in the Eaton model.
• Pore pressure derived from the Eaton model using acoustic impedances, and the suggested method is used to determine the pore pressure in the lower Barnett Shale for hydraulic fracture planning and simulation as well as wellbore stability.

1.1 Introduction

Pore pressure estimation is a crucial phase in oil and gas exploration, and pore pressure maps and profiles are essential for drilling, reservoir stimulation, and wellbore stability. The study by Zhang (2011), who examined the fracture gradient prediction methods and provided the minimum and maximum fracture pressures, is one of the many models that have been put out in the literature to estimate the pore pressure.

Bahmaei and Hossein have predicted the pore pressure using seismic velocity modeling, a case study from the Sefid-Zakhor gas field in Southern Iran is shown, the manufactured sonic logs are modified using the check shot interval velocity of Sefid-Zakhor well no. 1, and the final acoustic impedance model is converted to the velocity model by removing density. Finally, the velocity model is converted to pore pressure The results of the pore pressure model are validated by pore pressure data obtained by the modular formation dynamics tester (MDT) well test tool. Generally, the results show the normal trend for pore pressure in the area, except on the left side of the anticline in the 2D seismic section, because of tectonic uplifting.

The most popular empirical techniques for predicting anomalous pore pressure from well logs are then discussed. Additionally, employing depth-dependent normal compaction equations for pore pressure prediction in subsurface formations, Eaton's resistivity and sonic methods are modified. The modified techniques offer a considerably simpler method for managing typical compaction trendlines. The theoretical pore pressure modeling is essential to comprehend the mechanism of the anomalous pressure creation in addition to the empirical methods. It is suggested to use a theoretical pore pressure-porosity model.

In a paper about the predrill pore pressure prediction using seismic data, Sayers et al. (2002) demonstrated that a predrill estimate of pore pressure can be obtained from seismic velocities using a velocity-to-pore-pressure transform. However, the seismic velocities must be derived using techniques having sufficient resolution for well-planning purposes. There are noticeable disparities between the velocity field acquired using reflection tomography and that obtained using a traditional method based on the Dix equation for a deepwater Gulf of Mexico example.

Estimating the pore pressure from well-log data is one of the key jobs in shale gas reservoir characterization (Huffman 2002; Sayers et al. 2002; Sayers 2006). Several methods have been put out in the literature to do this.

A predrill estimate of the formation's pore pressure can be determined using seismic interval velocities by using a velocity-to-pore-pressure transform, according to Sayers et al. (2006)'s study on well-constrained seismic estimation of pore pressure with uncertainty. However, it is important to calculate seismic velocities using a technique that offers adequate spatial resolution for predrill well planning. Seismic interval velocities and well velocities can be combined to create a refined velocity field with an uncertainty that respects the well velocities that are currently available and can be used to accurately forecast pore pressure in a given area of interest. In this article, we propose a technique for pore pressure determination using seismic data generated from Eaton's model. Let us begin by outlining the suggested approach.

1.2 Methods and Application to Barnett Shale

1.2.1 Geological Setting

During a period of marine incursion brought on by the closure of the Lapetus Ocean Basin in the late Mississippian Age, the Barnett Shale was deposited across present-day North Central Texas. The Ouachita Thrust belt started to advance into the modern-day North Texas region toward the end of the Pennsylvanian. The sinking of the South American plate beneath the North American plate is what gave rise to the thrust belt. The foreland basin along the front of the Ouachita Thrust was produced by the thrust's emergence. Early investigations into the basin ascribed Barnett's thermal maturation to its history of burial and the thermal regimes connected to its depth of burial. As additional information became available, explorationists started to question this theory. An alternative theory put out by Bowker (2003), previously of Mitchell Energy/Devon, said that the Ouachita Thrust was responsible for the displacement of hot fluids from east to west, which fueled the maturation process. Figure 1.1 depicts the geologic column of the Mississippian and Pennsylvanian ages. The lower Barnett is the target for our shale gas reserve, and its top is 6650?ft deep (Givens and Zhao 2014).

Figure 1.1 Stratigraphic column of Barnett.

Source: Browning and Martin (1980).

1.2.2 Methods

Eaton's (1975) approach determines the vertical component of the effective stress from the seismic velocity v using the flowing equation and is typically used for pore pressure estimation (Eaton 1975; Bowers 1995):

sNormal and VNormal are the vertical effective stress and seismic velocity expected if the sediment is normally pressured, and n is an exponent that describes the sensitivity of velocity to effective stress, n = 1.2 (Kumar et al. 2012). The pore pressure is then given by:

According to Eaton's approach, the difference between the measured velocity and the velocity of sediments under normal pressure (VNormal) increases with depth as follows:

If Z is assumed to be the P wave's acoustic impedance, then Z is the result of the P wave's velocity and density.

Figure 1.2 Density versus velocity of the P wave in a log-log scale.

According to Gardner's model (Gardner et al. 1974), a power law connects density and velocity:

Figure 1.2 shows a cross-plot of the density versus the P wave's velocity in log-log scale, where a and b are lithology-related constants.

The following values of a and b were obtained from a linear fit of log of density versus log of velocity of the P wave.

The velocity of the P wave V and acoustic impedance are therefore connected by:

Therefore, the acoustic impedance and the velocity of the P wave are related by:

A normally pressurized formation's velocity is determined by:

where V0 and K are formation-related constants.

Figure 1.3 displays a cross-plot of the P wave's velocity versus depth. The values of V0 and K that result from fitting the P wave's velocity against depth linearly are shown here:

A typically pressurized formation's porous pressure is determined by:

where Z is the depth and GradPp is the pore pressure gradient which is equal to 0.52?psi/ft after Bowker (2007). Figure 1.4 displays the pore pressure versus depth in a lower Barnett Shale formation typically pressured formation. In a formation under normal pressure, the vertical tension is given by:

Figure 1.3 Velocity of the P wave versus the depth in the lower Barnett Shale gas reservoir.

where Z is the depth, ? is the density, and g = 9.81?m/s2 is the gravity...