Reservoir Characterization Through Synthetic Logs
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1 SPE Reservoir Characterization Through Synthetic Logs * Now with Chevron Shahab D. Mohaghegh, Carrie Goddard*, Andrei Popa, Sam Ameri, Moffazal Bhuiyan,
2 OUTLINE Objective Methodology Tools Approach Application Conclusion
3 OBJECTIVE To develop and test the feasibility of a methodology that provides a better and more cost effective way for reservoir characterization. The methodology uses the available well log data to accurately characterize effective porosity, fluid saturation and permeability.
4 OUTLINE Objective Methodology Tools Approach Application Conclusion
5 METHODOLOGY Tools Virtual Intelligence A collection of several analytical tools that attempts to imitate life. Exhibit an ability to learn and deal with new and dynamic situations. Possess attributes of reason such as generalization, discovery, association, and abstraction.
6 METHODOLOGY Tools Conventional Computing Soft Computing Bivalent Logic Fuzzy Logic Numerical Analysis Neurocomputing Probability Probabilistic Reasoning Differential Equations Genetic Algorithms Functional Analysis Belief Networks Mathematical Programming Chaos Approximation Theory Rough Sets Quantitative, Precise, Formal Qualitative, Imprecise, Informal Precision and certainty carry a cost
7 METHODOLOGY Tools Biologically Inspired Adaptive learning Parallel, distributed information processing _ P_NN_ S_V_D _S _ P_NN _RN_D
8 METHODOLOGY Tools
9 SPE OUTLINE Tools Objective Methodology Tools Approach Application Conclusion
10 METHODOLOGY Approach
11 METHODOLOGY Approach Use principles of Magnetic Resonance Imaging to measure effective porosity (MPHI) and irreducible water saturation (MBVI) in the reservoir rock. Difference between effective porosity and irreducible water saturation is called the Free Fluid Index (the producible fluid).
12 METHODOLOGY Approach MPHI Effective porosity is total porosity minus the clay bound porosity. MBVI Irreducible water saturation is the combination of clay bound water and water held due to the surface tension of the matrix material. MPERM A permeability log that is calculated from MPHI and MBVI.
13 METHODOLOGY Approach A B C z x y Alignment along B0 Tipping Dephasing
14 METHODOLOGY Approach ADVANTAGES DISADVANTAGES Identification of Fluid Types Quantification of Hydrocarbon Saturation Identification of Water in the formation Identification of hard to find pay zones No Radioactive Sources Used in Horizontal or Vertical Wells Used in a majority of muds COST High Salinity Water Based Muds Only Open Holes
15 METHODOLOGY Approach Magnetic Resonance logs play an important role by determining if low resistivity zones contain free or irreducible water. Magnetic Resonance logs can help to calculate a more realistic recoverable reserve by identifying high permeability portions of the pay.
16 METHODOLOGY Approach Mohaghegh and Popa showed that neural networks can be used to develop synthetic conventional logs. Mohaghegh and Richardson developed a process to accurately predict the values of magnetic resonance logs when the training, testing and validation data set belong to the same well.
17 METHODOLOGY Approach This study is a natural progression of the previous studies where a process is developed to predict and generate magnetic resonance logs for an entirely new well.
18 METHODOLOGY Approach Step 1: Identify wells that have both conventional logs (SP, GR, Induction...) and magnetic resonance logs (MPHI, MBVI, MPERM). Use data to develop a system that can produce synthetic (virtual) magnetic resonance logs. Develop Virtual MRI Logs
19 METHODOLOGY Approach Step 2: Test the validity of the process using a well that has both conventional and magnetic resonance logs and has not been used in the development process. Validate the Virtual MRI Logs
20 METHODOLOGY Approach Step 3: Apply the process to the wells in the field that have conventional logs but lack magnetic resonance logs. Develop a field strategy
21 OUTLINE Objective Methodology Tools Approach Application Conclusion
22 APPLICATION This study presents a proof of concept research. Using a highly heterogeneous and conventionally non-correlatable formation.
23 APPLICATION East Texas Rusk County Oak Hill Field Cotton Valley Formation
24 APPLICATION
25 APPLICATION Conventional Logs Conv. Porosity Logs Magnetic Res. Logs Well Name CALI SP GR ILD ILM SFL NPHI DPHI RHOB MBVI MPERM MPHI Beck_Fred_5 X X X X X X X X Christian_Alice_A5 X X X X X X X X X Christian_Alice_7 X X X X X X X X X Christian_Alice_6 X X X X X X X X X Busby_5 X X X X X X X X Busby_A5 X X X X X X X X Christian_Alice_1 X X X X X X X X Christian_Alice_A2 X X X X X X X X X Christian_Alice_4 X X X X X X X X Busby_2 X X X X X X Busby_1 X X X X X X X X X Busby_A1 X X X X X X X X Busby_4 X X X X X X X X Christian_Alice_A6 X X X X X X X Busby_A2 X X X X X X X X Busby_3 X X X X X X X X X Christian_Alice_A4 X X X X X X Beck_Fred_3 X X X X X X X X X Busby_A3 X X X X X X X X X Beck_Fred_2 X X X X X X X X X Beck_Fred_1 X X X X X X X X X Christian_Alice_3 X X X X X X X X Christian_Alice_2 X X X X X X X X Beck_Fred_4 X X X X X X Christian_Alice_A3 X X X X X Christian_Alice_A1 X X X X X X X
26 APPLICATION
27 APPLICATION
28 APPLICATION Neural Network Input Depth X Coordinate Y Coordinate CALI GR ILD ILM SFL SP OUTPUT Synthetic Porosity Logs
29
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31 APPLICATION Conventional Logs Conv. Porosity Logs Magnetic Res. Logs Well Name CALI SP GR ILD ILM SFL NPHI DPHI RHOB MBVI MPERM MPHI Beck_Fred_5 X X X X X V V V X X X Christian_Alice_A5 X X X X X X V V V X X X Christian_Alice_7 X X X X X X V V V X X X Christian_Alice_6 X X X X X X V V V X X X Busby_5 X X X X X V V V X X X Busby_A5 X X X X X V V V X X X Christian_Alice_1 X X X X X X X X Christian_Alice_A2 X X X X X X X X X Christian_Alice_4 X X X X X X X X Busby_2 X X X X X X Busby_1 X X X X X X X X X Busby_A1 X X X X X X X X Busby_4 X X X X X X X X Christian_Alice_A6 X X X X X X X Busby_A2 X X X X X X X X Busby_3 X X X X X X X X X Christian_Alice_A4 X X X X X X Beck_Fred_3 X X X X X X X X X Busby_A3 X X X X X X X X X Beck_Fred_2 X X X X X X X X X Beck_Fred_1 X X X X X X X X X Christian_Alice_3 X X X X X X X X Christian_Alice_2 X X X X X X X X Beck_Fred_4 X X X X X X Christian_Alice_A3 X X X X X Christian_Alice_A1 X X X X X X X
32 APPLICATION
33 APPLICATION
34 APPLICATION Neural Network Input»Depth» X Coordinate» Y Coordinate»CALI»GR»ILD»ILM»SFL»SP» Synthetic Porosity Logs OUTPUT Synthetic MRI Logs
35
36
37
38 APPLICATION Reserve estimate using virtual and actual magnetic resonance logs. Actual Magnetic Resonance Log Data 139,324 MCF/acre Difference = 0.5 % Virtual Magnetic Resonance Log Data 138,630 MCF/acre
39 CONCLUSIONS A new methodology is introduced that has the potential to significantly reduce the cost of reservoir characterization from well logs. This methodology uses the conventional well logs and generates virtual or synthetic magnetic resonance logs for all the wells in a field.
40 CONCLUSIONS The development process requires that a handful of wells in a field be logged using the magnetic resonance logging tools. Then the data generated from the magnetic resonance logging process is coupled with the conventional log data and used to develop an intelligent, predictive model.
41 CONCLUSIONS The process helps engineers to acquire a better handle on the reservoir characteristics at a fraction of the cost of running magnetic resonance logs on all the wells in the field. This is especially true and beneficial for fields that have many producing wells that already have been cased.
42 CONCLUSIONS It was also demonstrated that virtual magnetic resonance logs could provide reserve estimates that are highly accurate when compared to the reserve estimates that can be acquired from actual magnetic resonance logs.
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