Shale Gas Reservoirs Characterization Using Neural ... - ScienceDirect

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ScienceDirect Energy Procedia 59 (2014) 16 – 21

European Geosciences Union General Assembly 2014, EGU 2014

Shale gas reservoirs characterization using neural network Sid-Ali Ouadfeula*, Leila Aliouaneb b

a Algerian Petroleum Institute, IAP, Avenue 1er Novembre, Boumerdes 35000, Algeria Labophyt,Faculty of Hydroarbon and Chemistry, University Mhamed Bougara, UMBB, Avenue de l’indépendance, Boumerdes 3500, Algeria

Abstract In this paper, a tentative of shale gas reservoirs characterization enhancement from well-logs data using neural network is established. The goal is to predict the Total Organic carbon (TOC) in boreholes where the TOC core rock or TOC well-log measurement does not exist. The Multilayer Perceptron (MLP) neural network with three layers is implanted. The MLP input layer is constituted with five neurons corresponding to the natural Gamma ray, Neutron porosity and sonic P and S wave slowness. The hidden layer is composed with nine neurons and the output layer is formed with one neuron corresponding to the TOC log. Application to two horizontal wells drilled in Barnett shale formation where the well A is used as a pilot and the well B is used for propagation clearly shows the efficiency of the neural network method to improve the shale gas reservoirs characterization. The established formalism plays a high important role in the shale gas plays economy and long term gas energy production. © Published by Elsevier Ltd. Ltd. This is an open access article under the CC BY-NC-ND license ©2014 2014The TheAuthors. Authors. Published by Elsevier (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Austrian Academy of Sciences. Peer-review under responsibility of the Austrian Academy of Sciences Keywords:TOC; Prediction; MLP; Prediction; Shmoker

1. Introduction Shale gas reservoirs have becoming a very important source of energy with increasing demand in the last decade, this kind of unconventional reservoirs requires an advanced technology for exploration and production. In exploration geophysics, shale gas reservoirs require some special kind of seismic and well-logs data tools. For the seismic method, shale gas reservoirs require a wide azimuth 3D design to realize the so-called Amplitude Versus Azimuth (AVAZ) method. Many well-logging tools are implanted to study this kind of unconventional reservoirs; we can cite for example the ECS and Lithoscaner tools.

* Corresponding author. E-mail address: [email protected]

1876-6102 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Austrian Academy of Sciences doi:10.1016/j.egypro.2014.10.343

Sid-Ali Ouadfeul and Leila Aliouane / Energy Procedia 59 (2014) 16 – 21

All these implanted methods and tools are implanted to identify the sweet spots areas inside the shale gas reservoirs, these areas are characterized by good geomechanical parameters for hydraulic fracture and good geochemical parameters, like the Total Organic Carbon (TOC) and good maturity. Many techniques are used to estimate the TOC, some of them are based on the direct measurement of the percentage of each chemical element of the rock including the Carbon (C), by subtraction of the mineral carbon from the measured carbon concentration from the rock we can obtain an estimated value of the organic carbon which is the TOC. Two experimental models are suggested for estimation of TOC from well-logs data, the first one is the so called the Passey’s model (Passey et al, 1990), this last uses the Deep Resistivity (Rt) and the Slowness of the P wave ¨W . The second model is the so-called the Schmoker’s model, it relates the TOC to the inverse of the Bulk GHQVLW\ ȡb) by a linear relationship. Estimation of the TOC using the Schmoker’s model requires always a continuous measurement of the Bulk density at each depth of the reservoir which is not always easy in well-logs data. In this paper, we suggest the use of the Artificial Neural Network (ANN) to predict the TOC form well-logs data in case of absence of measurement of the Bulk density log. We start the paper by explaining the principle of neural network method, after that we explain in details the different methods that are used for the determination of the TOC. The implanted neural network machine is applied to two horizontal wells drilled in the Barnett shale, we end the paper by results interpretation and conclusions.

2. The total organic carbon in shale gas reservoirs The Total Organic Carbon (TOC) is the amount of organic carbon in the source rock, there is no unit to quantify it. It is very important to have a high TOC (>5%) to talk about a good shale plays and sweet spots. Three methods are used for TOC measurement and estimation, the first is based on the direct measurement in the laboratory. The second one is based on the direct measurement using a well-logging tool. The last one is based on the empirical measurement, in this case two methods are used, the first one is the so-called the Passey’s method and the second one is called the Schmoker’s method.

2.1. The Passey’s method: CuUUHQWO\ǻORJ5WHFKQLTXH[1] is the most common method and widely used for TOC determination in shale gas SOD\V%DVLFDOO\WKHWKUHHSRURVLW\ZHOOORJVGHQVLW\QHXWURQDQGVRQLFFDQEHXVHGWRFDOFXODWHWKHǻORJ5EDVHGRQ the separation between the deep resistivity curve and the porosity logs, which can be converted into TOC through the level of organic maturity parameter (LOM). Usually LOM needs laboratory experiment to measure. In practice FRUH72&DQGFDOFXODWHGǻORJ5DVVLVWLQFDOLEUDWLRQWRHVWLPDWHWKH/20,QǻORJ5WHFKQLTXHSRURVLW\HIIHFWVDUH minimized. However, sometimes the Passey’s well logging baselines determination is very challenging. On the other KDQGǻORJ5WHFKQLTXHFDQEHXVHGIRUWKHZHOOVZLWKRQHSRURVLW\ORJRUPXOWLSRURVLW\ORJV If multi porosity log FXUYHVDUHXVHGLQǻORJ5WHFKQLTXHHVWLPDWHG72&YDOXHVPLJKWEHGLIIHUHQW [1]. 2.2. Schmoker’s method Two widely used empirical approaches have been developed to quantitatively estimate TOC from log data. The first was developed in Devonian shales using bulk density logs [2, 3] and was later refined in Bakken shales [4]. Based on the response of the bulk density measurement to low-density organic matter (~1.0 g/cm3), the Schmoker’s method, as it is commonly called, computes TOC [3]:

TOC

154.497

Ub

 57.261

17

18

Sid-Ali Ouadfeul and Leila Aliouane / Energy Procedia 59 (2014) 16 – 21

where ȡb is the bulk density in g/cm3 and TOC is reported in wt%. This equation assumes a constant mineral composition and porosity throughout the formation. Although the method was developed and refined based on specific environments, it is frequently used for TOC estimation in a wide variety of shale formations. 3. Artificial neural network 3.1. The Multilayer perceptron Multilayer feed-forward networks form an important class of neural networks. Typically the network consists of a set of sensory units or input nodes, that constitute the input layer, one or more hidden layers of neurons or computation nodes, and an output layer. Multi-layer Perceptron (MLP) neural networks with sufficiently many nonlinear units in a single hidden unit layer have been established as universal function approximators. The advantages of the MLP are: Hidden unit outputs (basis functions) change adaptively during training, making it unnecessary for the user to choose them beforehand. The number of free parameters in the MLP can be unambiguously increased in small increments by simply increasing the number of hidden units. The basic functions are bounded making overflow errors and round-off errors unlikely. The MLP is a feedforward network consisting of units arranged in layers with only forward connections to units in subsequent layers. The connections have weights associated with them. Each signal travelling along a link is multiplied by its weight. The input layer, being the first layer, has input units that distribute the inputs to units in subsequent layers. In the following (hidden) layer, each unit sums its inputs and adds a threshold to it and nonlinearly transforms the sum (called the net function) to produce the unit output (called the activation). The output layer units often have linear activations, so that output activations equal net function values. The layers sandwiched between the input and the output layers are called hidden layers, and the units in the hidden layers are called hidden units [5, 6].

4. Application to real data The proposed idea is applied to well-logs data of two horizontal wells drilled in the Barnett shale, let us start by describing the geological setting of this shale play. 4.1. Geological setting The Barnett Shale was deposited over present day North Central Texas during the late Mississippian Age in a time marine transgression caused by the closing of the lapetus Ocean Basin. By the end of the Pennsylvanian the Ouacita Thrust belt began encroaching into the present day North Texas area. The thrust belt owes its existence to the subduction of the South American plate under the North American plate The Ouachita Thrust's emergence created the foreland basin along the front of the thrust. Early studies of the basin attributed thermal maturation of the Barnett to burial history and the thermal regimes associated with depth of burial. Explorationists began to doubt this hypothesis as more data became available. Kent [7] formerly of Mitchell Energy/Devon proposed a different model suggesting the maturation process was driven by displacement of hot fluids, from east to west, associated with the Ouachit Thrust. Fig. 1 shows the stratigraphic column of Mississippian and the Pennsylvanian ages, our shale gas reservoir target is the lower Barnett, the top of the reservoir is located at 6650m [8].

Sid-Ali Ouadfeul and Leila Aliouane / Energy Procedia 59 (2014) 16 – 21

Fig. 1 Stratigraphic column of Mississippian and the Pennsylvanian ages [9]

4.2. Data processing Data of two horizontal wells drilled in the Barnett Shale are processed using the Multilayer Perceptron (MLP) neural network machine, data of the horizontal well H01 are used as an input of a MLP machine composed with three layers, an input layer with four neurons, an output layer with four neurons and a hidden layer with ten neurons. Number of neurons in the hidden layer is obtained after many numerical experiences. Well-logs data used as an input are: The neutron Porosity (NPOR), the Bulk Density (Rhob); the slowness of the compression wave (DTC), the slowness of the shear wave (DSTM). In this case, we suppose that we have not a density log, however the TOC obtained using the Schmoker’s model is used as an output to train the MLP machine. Fig. 2 shows the recorded well-logs data and the Schmoker’s TOC. Track 01 is the depth, track 02 is the Gamma log, the third track is the neutron porosity, however the five and the six tracks are the slowness of the compression and the shear wave. The last track is the calculated TOC using the Schmoker’s model. The couple input-desired output is used to train the MLP machine, after the training step weights of connections are calculated. To check the efficiency of the neural machine data of another horizontal well H02 drilled in the neighborhood H01 are used as an input (see Fig. 3).

5. Results interpretation and conclusion Data of the well H02 are propagated through the neural network machine; at this stage desired output is not required and the weights of connections between neurons of the Multilayer Perecptron neural network machine are used. Fig. 4 is comparison between the calculated TOC using the MLP machine after propagation of the well-logs data and the calculated TOC by Schmoker’s method using the density log of the well H02. It is clear that the implanted neural network machine is able to provide excellent results that are very close to the calculated TOC using the Schmoker’s model. Obtained results clearly show the ability of the artificial neural network to predict a Total Organic Carbon (TOC) in case of absence of the density log or a discontinuous measurement of this log.

19

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The implanted neural machine is able to replace the Schmoker’s model where the density log does not exist or there are big discontinuities in the log. Our approach can be greatly used in the Basin modeling, Sweet spots researches and estimation of the Kerogen volume.

Fig .2 Well-logs data of the training horizontal well (01) Depth

Fig .3 Recorded well-logs data for the second horizontal well H02 drilled in the neighborhood of the well H01

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TOC_Shmoker Predicted_TOC

8

TOC(%)

6

4

2

0 6800

7000

7200

7400

7600

7800

8000

8200

Depth(m) Fig. 4 Calculated TOC using the MLP machine compared with the Shmoker’s TOC.

References [1] Passey Q, Bohacs K, Esch W, Klimentidis R, Sinha S. From Oil-Prone Source Rock to Gas-Producing Shale Reservoir - Geologic and Petrophysical Characterization of Unconventional Shale-Gas Reservoirs: SPE 131350, CPS/SPE International Oil & Gas Conference and Exhibition, Beijing, China June 8-10 1990. [2] Schmoker J. Determination of Organic Content of Appalachian Devonian Shales from Formation-Density Logs, Am Assoc Petrol Geol Bull 1979; 63: 1504-1537. [3] Schmoker J. Organic Content of Devonian Shale in Western Appalachian Basin: Am Assoc Petrol Geol Bull 1980; 64: 2156-2165. [4] Schmoker J, Hester T. Organic Carbon in Bakken Formation, United States Portion of Williston Basin: Am Assoc Petrol Geol Bull 1983; 67: p. 2165-2174. [5] Ouadfeul S, Aliouane L. Lithofacies Classification Using the Multilayer Perceptron and the Self-organizing Neural Networks, Lecture Notes in Computer Science 2012; 7667: 737-744. [6] Ouadfeul S, Aliouane L. Lithofacies prediction from well log data using a multilayer perceptron (MLP) and Kohonen's self-organizing map (SOM) – a case study from the Algerian Sahara, Pattern Recog Phys 2013; 01: 01: 59-62. [7] Kent B. Recent Development of the Barnett Shale Play, Forth Worth Basin.West Texas Geol Soc Bull 2003; 2: 6. [8] Givens N, Zhao H. The Barnett Shale: Not so Simple after all, Republic Energy 2014. [9] Browning DW, Martin CA. Geology of North Caddo Area, Stephens County, Texas: in Martin C.A. ed., Petroleum Geology of the Fort Worth Basin and Bend Arch Area, Dallas Geological Society; 1980, p. 315–330. [10] Passey Q, Creaney S, Kulla J, Moretti F, Stroud J. A Practical Model for Organic Richness from Porosity and Resistivity Logs: Am Assoc Petrol Geol Bull 1990; 74: 1777-1794.