Production logging inversion based on das/dts

A method of optimizing oil production, said method comprising:

• a) providing one or more fiber optic cables operably coupled to one or more interrogators in a well in an oil reservoir;
• b) measuring low-frequency Distributed Acoustic Sensing (LFDAS) data in said well during a time period of constant flow and during a time period of no flow and during a time period of perturbation of flow, wherein said perturbation of flow is created by thermal perturbations;
• c) simultaneously measuring Distributed Temperature Sensing (DTS) data from said well during said time period of constant flow and during said time period of no flow and during said time period of perturbation of flow;
• d) using one or more of equations 1-6 or their mathematical equivalents:
• i) inverting U and G using a gradient-descent based optimization while holding Pi fixed;
• ii) inverting Pi using a Markov chain Monte Carlo optimization;
• iii) repeating step i-ii) thousands of times to generate thousands of initial models;
• iv) randomly perturbing Pi of an initial model to create a new model and retaining said new model as a final model if a penalty function ∈2 for said new model is smaller than that of said initial model, and otherwise abandoning said new model;
• v) repeating step iv for each of said initial models to generate thousands of final models;
• vi) statistically analyzing said thousands of final models to obtain production allocation results;
• and
• e) using said production allocation to optimize production of hydrocarbon from said well and future wells in said reservoir.

A method of predicting hydrocarbon production allocation from a reservoir, said method comprising:

• a) providing one or more fiber optic cables operably coupled to one or more interrogators in a well in a reservoir, wherein one or more fiber optic cables are permanently installed in said well;
• b) measuring low-frequency Distributed Acoustic Sensing (LFDAS) data in said well during a time period of constant flow and during a time period of no flow, and during a time period of perturbations of flow;
• c) simultaneously measuring Distributed Temperature Sensing (DTS) data from said well during said time period of constant flow and during said time period of no flow and during said time period of perturbations of flow;
• d) using one or more of equations 1-6 or their mathematical equivalents:
• i) inverting U and G using a gradient-descent based optimization while holding Pi fixed;
• ii) inverting Pi using a Markov chain Monte Carlo optimization;
• iii) repeating step i-ii) thousands of times to generate thousands of initial models;
• iv) randomly perturbing Pi of an initial model to create a new model and retaining said new model as a final model if a penalty function ∈2 for said new model is smaller than that of said initial model, and otherwise abandoning said new model;
• v) repeating step iv for each of said initial models to generate thousands of final models;
• and
• vi) statistically analyzing said final models to obtain hydrocarbon production allocation results from said well.