Contents & References of The use of artificial neural networks to recognize the model of horizontal wells in oil reservoirs using well test data
List:
1- Introduction. 2
1-1- An introduction to reservoir engineering. 2
1-2- Oil tanks and exploitation of oil tanks. 3
1-3- Definitions of types of tanks using fuzzy diagrams. 5
1-4- An overview of reservoir rock properties. 8
1-4-1- degree of porosity. 8
1-4-2-Isothermal compressibility. 8
1-4-3- degree of stone saturation. 9
1-5- An introduction to well testing. 9
1-5-1- Factors affecting well testing. 12
1-5-1-1- shell factor. 12
- Coefficient of hydraulic fracturing shell. 12
- Partial well completion and partial meshing. 12
1-5-1-2- The effect of storage in the well. 14
- rule of thumb. 15
1-5-1-3- Permeability or permeability. 15
1-5-1-4- How the fluid moves inside the porous medium. 15
1-5-1-5- Tank borders. 16
- internal border. 16
- the outer border of the tank. 16
1-5-2- Types of well tests. 17
1-5-2-1- periodic production tests (daily measurement of flow rate and pressure). 17
1-5-2-2- Tests to measure well productivity. 18
1-5-2-2-1- for oil tanks. 18
1-5-2-2-2- for gas tanks. 19
- Production efficiency index test. 19
- Flow performance test into the well. 19
- Flow rate changes during long production time. 19
- Flow rate changes in short production time. 19
- Flow rate changes in the short time of producing and closing the well. 20
1-5-2-3- transient pressure tests (pressure with time). 20
1-5-2-3-1- pressure rise test. 21
- Ideal pressure rise test. 22
- Real pressure rise test. 23
- Deviation from the ideal state. 24
- Methods of interpreting the pressure rise test. 24
1-5-2-3-2- flow test. 26
Problems of flow well testing. 28
1-5-3- The use of derivative diagrams in the analysis of well tests. 29
1-5-3-1- Examples of the application of pressure derivative curves. 29
1-6- Types of wells in reservoirs. 32
1-6-1- Vertical wells. 32
1-6-2-wells with hydraulic fracture. 32
1-6-3- horizontal well. 33
1-6-3-1- Periodic vertical radial flow. 34
1-6-3-2- Intermediate linear flow period. 35
1-6-3-3- Periodicity of the end pseudo-radial flow. 35
1-6-4 - Time equations of different regimes in a horizontal well. 36
1-6-4 - Pressure analysis in a horizontal well. 37
1-7-1- Pressure reduction test. 37
- Pressure response in the initial vertical radial flow period. 37
- Pressure response in the intermediate linear flow period. 37
- The pressure response in the terminal pseudo-radial flow period. 37
1-7-1- Pressure surge test. 38
- Pressure response in the initial vertical radial flow period. 38
- Pressure response in the intermediate linear flow period. 38
- Pressure response in the terminal pseudo-radial flow period. 38
1-8- neural networks. 38
1-8-1- brain structure. 39
1-8-2- Mathematical model of a neuron. 40
1-8-3-Learning the network. 42
A) Learning with the supervisor. 42
b) Unsupervised learning. 42
c) Reinforcement learning. 42
1-8-4- Division based on structure. 42
a) Pre-consumer networks. 42
b) Recursive networks. 43
1-8-5- Perceptron network. 43
1-8-6- The order of providing data to the network. 44
1-8-7- transfer function. 44
1-8-8- End of training. 45
1-8-9- the number of neurons in the layers 46
1-8-10- goodness of fit criteria. 46
- Regression analysis. 46
- Correlation coefficient. 46
- the mean square of the squared error. 47
- Average relative errors. 47
2- Review of past works. 49
2-1- Work done on neural networks. 49
2-2- Works done on horizontal wells. 59
3- Collecting well test data. 66
3-1- Introduction. 66
3-2- Parameters needed to enter into the software 67
3-3- Well test parameters of reservoir models. 68
3-3- 1-Using the experiment design method to generate primary data. 69
3-3-2- Convert pressure data to pseudo-pressure and derive from them 70
3-4-Normalization. 71
3-5- neural network structure. 71
3-6- Considered models 73
- Pressure homogeneous tank71
3-6- Considered models 73
- Constant pressure homogeneous tank, without flow and without limited boundary. 73
- Constant pressure, no-flow homogeneous reservoir with a single constant pressure fault boundary. 74
- Constant pressure homogeneous reservoir, no flow with single fault no flow. 75
- Constant pressure double porosity tank, without flow and without limited boundary. 75
- constant-pressure, no-flow double-porosity reservoir with a single constant-pressure fault boundary. 77
- constant-pressure, no-flow double porosity reservoir with single no-flow fault boundary. 78
- Flowless dual porosity reservoir with single constant pressure fault boundary. 79
- Double porosity, no-flow reservoir with a single no-flow fault boundary. 79
4- Discussion and results. 82
4-1- Introduction. 82
4-2- Determining the optimal structure of the forward network 82
4-2-1- Network training. 85
4-3- Discussion and results. 87
4-3-1- Testing the network with test data. 87
4-3-2- Checking the endurance of the network against noisy graphs. 89
5- Conclusion and suggestions. 99
5-1- Introduction. 99
5-2- Results. 99
- Results related to data simulation by software. 99
- Results related to artificial neural network. 99
5-3-2- Suggestions. 100
Resources
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