CN-121977971-A - Crude oil viscosity prediction method, system and device

CN121977971ACN 121977971 ACN121977971 ACN 121977971ACN-121977971-A

Abstract

The invention discloses a crude oil viscosity prediction method, a system and a device, and relates to the technical field of crude oil viscosity prediction, wherein the method comprises the steps of obtaining nuclear magnetic resonance T 2 spectrum of a target crude oil sample, and calculating a logarithmic average value based on the T 2 spectrum; the method comprises the steps of constructing a base oil phase viscosity model representing no wax or neglected wax influence based on a logarithmic average value and absolute temperature of a T 2 spectrum, constructing a wax phase viscosity model independent of the base oil phase viscosity model, determining a corresponding wax solubility factor according to a target temperature, carrying out weighted average fusion on the base oil phase viscosity model corrected by the wax solubility factor and the wax phase viscosity model in a viscosity logarithmic space by taking a known wax amount fraction as a weight to obtain a final total viscosity predicted value, and introducing a wax content correction mechanism based on nuclear magnetic resonance T 2 spectrum analysis to more accurately reflect actual viscosity characteristics of crude oil under stratum conditions.

Inventors

QIN ZHEN
ZHANG XINYI
LI HONGXING
DENG CHENGXIANG
XIAO KUN
ZHANG XIAOFENG
HU XUDONG
ZHU YUNFENG

Assignees

东华理工大学南昌校区

Dates

Publication Date: 20260505
Application Date: 20260408

Claims (9)

1. A method for predicting the viscosity of crude oil, comprising the steps of: Acquiring nuclear magnetic resonance T 2 spectrums of crude oil samples in a target area at a plurality of temperature points, and calculating a logarithmic average value corresponding to each temperature point based on the T 2 spectrums; Fitting and constructing a base oil phase viscosity model based on the absolute temperature and logarithmic average value corresponding to each temperature point and the corresponding base viscosity experimental value, wherein the base oil phase viscosity model is used for representing the functional relation between the viscosity and the logarithmic average value and the absolute temperature when the crude oil sample does not contain wax or the contribution of the wax to the viscosity is ignored; Describing the exponential change relation of the viscosity of a crude oil sample along with the temperature by adopting an Arrhenius equation, and constructing a wax phase viscosity model; Determining a wax solubility factor for dynamically characterizing a decrease in viscosity contribution as a wax phase in the crude oil dissolves with increasing target temperature, based on a target temperature of the target region; And in a viscosity logarithmic space, using known wax amount fraction as weight, carrying out weighted average fusion on the logarithm of the oil phase effective viscosity contribution value and the logarithm of the wax phase viscosity obtained according to the wax phase viscosity model to obtain the crude oil total viscosity predicted value at the target temperature.
2. The method for predicting the viscosity of crude oil according to claim 1, wherein the calculation of the logarithmic average value corresponding to each temperature point based on the T 2 spectrum is expressed as: ; Where T 2,i is the ith relaxation time and f i is the corresponding signal amplitude.
3. The method of claim 2, wherein the base oil phase viscosity model is expressed as: ; wherein T K is absolute temperature, j represents temperature point, a and b are model parameters, Is the viscosity of the base oil phase.
4. The method of claim 1, wherein the wax phase viscosity model is expressed as: ; Wherein, the For the initial viscosity of the wax at the reference temperature, E a is the activation energy, R is the gas constant, T is the absolute temperature, Is wax phase viscosity.
5. A method of predicting the viscosity of crude oil as set forth in claim 1 wherein the wax solubility factor for dynamically characterizing the viscosity contribution decay as the wax phase in the crude oil dissolves with increasing target temperature is determined from the target temperature of the target zone using a piecewise exponential decay function It is expressed as: ; wherein, T cr is critical temperature, T K is Kelvin temperature in K, j is temperature point.
6. The method for predicting the viscosity of crude oil according to claim 1, wherein in the viscosity log space, the known wax amount fraction is used as a weight, and the logarithm of the effective viscosity contribution value of the oil phase and the logarithm of the wax phase viscosity obtained according to the wax phase viscosity model are weighted and averaged to obtain the predicted value of the total viscosity of crude oil at the target temperature, and the process is expressed as: ; wherein i refers to the sample or well section number, j refers to the temperature point, Represent the first The waxy fraction of the individual crude oil samples, As a predicted value of the total viscosity of the liquid, Is the viscosity of the wax phase, and the viscosity of the wax phase is the viscosity of the wax phase, Is an effective viscosity contribution value of the oil phase.
7. A crude oil viscosity prediction system, comprising: the acquisition module is used for acquiring nuclear magnetic resonance T 2 spectrums of the crude oil sample in the target area at a plurality of temperature points and calculating a logarithmic average value corresponding to each temperature point based on the T 2 spectrums; The system comprises a base oil phase viscosity model construction module, a base oil phase viscosity model analysis module and a base oil phase viscosity model analysis module, wherein the base oil phase viscosity model construction module is used for fitting and constructing a base oil phase viscosity model based on absolute temperature and logarithmic average values corresponding to all temperature points and corresponding base viscosity experimental values; the wax phase viscosity model construction module is used for describing the exponential change relation of the viscosity of the crude oil sample along with the temperature by adopting an Arrhenius equation to construct a wax phase viscosity model; Determining a wax solubility factor for dynamically characterizing a decrease in viscosity contribution as a wax phase in the crude oil dissolves with increasing target temperature, based on a target temperature of the target region; And in a viscosity logarithmic space, using the known wax amount fraction as a weight, carrying out weighted average fusion on the logarithm of the oil phase effective viscosity contribution value and the logarithm of the wax phase viscosity obtained according to the wax phase viscosity model to obtain a crude oil total viscosity predicted value at a target temperature.
8. A crude oil viscosity prediction computer device comprising a memory, a processor and a computer program stored in the memory, the processor implementing the steps of the crude oil viscosity prediction method of any of claims 1-6 when the computer program is executed.
9. A readable storage medium, characterized in that the readable storage medium stores a computer program comprising program instructions for performing the steps of the crude oil viscosity prediction method according to any of claims 1-6 when executed by a processor.

Description

Crude oil viscosity prediction method, system and device Technical Field The invention relates to the technical field of crude oil viscosity prediction, in particular to a crude oil viscosity prediction method, a crude oil viscosity prediction system and a crude oil viscosity prediction device. Background Crude oil viscosity is one of the important parameters for evaluating the properties and development potential of oil reservoir fluids, and the change characteristics reflect the flow mechanism and interaction process of the fluids under complex geological conditions. Too high viscosity can lead to the reduction of the fluidity of oil reservoir fluid and increase the recovery difficulty, so how to rapidly and accurately predict the viscosity of crude oil is always a research hotspot in the field of oil and gas exploration and development. The existing crude oil viscosity acquisition mode mainly comprises a first laboratory determination method, wherein the crude oil viscosity is directly measured through laboratory viscosimeter and other experimental equipment. The method has higher precision, but the testing process needs to collect the oil sample and be completed in a laboratory, so that the time consumption is longer, the cost is higher, and the on-site quick prediction and real-time evaluation are difficult to realize. 2. An indirect prediction method based on an empirical formula or logging parameters is that an empirical relationship is established between fluid physical parameters, logging parameters or Nuclear Magnetic Resonance (NMR) data and viscosity, so that indirect estimation of viscosity is realized. Among them, the method based on nuclear magnetic resonance transverse relaxation time (T 2) spectrum is receiving attention because it can reflect the microscopic relaxation characteristics and pore distribution characteristics of the fluid. At present, a crude oil viscosity prediction method based on an NMR T 2 spectrum adopts a T 2 spectrum characteristic parameter method, wherein a T 2 logarithmic average value (T 2,LM）、T2 weighted average value or empirical relation between characteristic peak position and crude oil viscosity) is used for prediction, and researches show that the T 2 logarithmic average value and the crude oil viscosity are in inverse relation, and the method can be used for quickly establishing a prediction formula, namely, the T 2 spectrum distribution fitting method is used for extracting relaxation characteristics of free fluid and bound fluid by carrying out multimodal fitting or model decomposition on a T 2 spectrum distribution curve so as to indirectly infer the crude oil viscosity. However, aiming at crude oil with high wax content, wax molecules can be separated out, crystallized and structurally acted under the formation temperature condition, the rheological property of the crude oil is obviously changed, so that the viscosity response of the crude oil is different from that of common crude oil, the nonlinear and decisive influence of the wax content and the physical process of the wax content along with the temperature change on the viscosity of the crude oil can be easily ignored in the prediction process by the existing method, and a viscosity empirical formula is built only by means of the T 2 spectral characteristic parameters, so that larger deviation can be generated, and the prediction precision is insufficient. Disclosure of Invention Aiming at the problems that the prior art ignores the nonlinear and decisive influence of the wax content and the physical process of the wax content along with the temperature change on the viscosity of the crude oil, so that larger deviation is generated and the prediction precision is insufficient, the invention provides a crude oil viscosity prediction method, a system and a device. A method for predicting the viscosity of crude oil, comprising the steps of: Acquiring nuclear magnetic resonance T 2 spectrums of crude oil samples in a target area at a plurality of temperature points, and calculating a logarithmic average value corresponding to each temperature point based on the T 2 spectrums; Fitting and constructing a base oil phase viscosity model based on the absolute temperature and logarithmic average value corresponding to each temperature point and the corresponding base viscosity experimental value, wherein the base oil phase viscosity model is used for representing the functional relation between the viscosity and the logarithmic average value and the absolute temperature when the crude oil sample does not contain wax or the contribution of the wax to the viscosity is ignored; Describing the exponential change relation of the viscosity of a crude oil sample along with the temperature by adopting an Arrhenius equation, and constructing a wax phase viscosity model; Determining a wax solubility factor for dynamically characterizing a decrease in viscosity contribution as a wax phase in the crude oil dissolves with