CN105306066A - Method of lossless compression of oil well data based on Taylor series estimation - Google Patents

Abstract

The invention discloses a method of lossless compression of oil well data based on Taylor series estimation, and belongs to the field of data compression processing. According to the method, an oil well data curve is fitted through decomposition of Taylor series, then, backward estimation is carried out, and lossless compression of data is realized through transmitting estimation error of a fitted value and an actual value. Moreover, according to actual application situation, a method of adaptively selecting Taylor series decomposition order of minimum compression rate is designed. The method of lossless compression of oil well data based on Taylor series estimation is suitable for an oil field data remote transmission system, and can effectively reduce data load of a transmission network.

Description

Based on the well data lossless compression method that Taylor series are estimated

Technical field

This method relates to the well data Lossless Compression estimated based on Taylor series, effectively can reduce the load of well data transmission network.

Background technology

Due to the intensification of Oilfield Information degree, transmitted data amount sharply increases, but is subject to the restriction of oil field geographical environment, and these information are mainly through wireless network transmissions.A wireless network for internal proprietary, as ZigBee-network; Another kind is third party's Operation Network, as GPRS or 3G network.The former bandwidth is less, is difficult to the transmission quantity that load is increasing; The latter is with roomy, but belongs to charge network, takies civilian resource.Therefore, the mode of directly transmission, has not been suitable for the demand of digitlization oil field big data quantity.The method of more existing research oil well data compressions at present.RDP (Ramer-Douglas-Peucker) algorithm, expresses the key point of this curve shape feature by only retaining being enough on curve, realize data compression, but data precision exists obviously error at deletion point place, and belongs to lossy compression method.Somebody proposes one and does singular value decomposition to mass data, carrys out approximate fits data, reach the object of data compression with the base vector obtained, but algorithm amount of calculation is large, cannot meet the real-time Transmission of well data.In addition, some classical general data compression algorithms: huffman compression coding, arithmetic coding, Run-Length Coding, lzw algorithm etc., these algorithms all do not consider the feature of well data, effectively can not realize the compression of well data.

Summary of the invention

In order to realize the high efficiency of transmission of well data, taking into full account its data and curves correlation, proposing a kind of new lossless compression algorithm.First utilizing Tavlor series expansion to simulate well data curve, then carry out backward estimation, by transmitting the evaluated error of match value and actual value, realizing the Lossless Compression of data.And according to practical situations, design the method for the Tavlor series expansion exponent number of adaptively selected minimal compression rate.

The object of the invention is to be achieved through the following technical solutions.

The well data lossless compression method estimated based on Taylor series of the present invention, step is:

1) according to oil well actual acquired data situation, by calculating minimal compression rate, the optimal factor M of Tavlor series expansion is determined.

Described compression ratio calculates, and is the data after being compressed by following steps and the data before compression, the bit wide ratio after quantification.

2) to the data collected, by M rank Taylor series expansion, coefficient (a is solved ₀, a ₁..., a _m).The coefficient of described Tavlor series expansion is determined to utilize front M+1 known point, lists M+1 decomposition formula, obtains by separating math equation calculating.

3) according to step 1) and step 2) M that obtains and (a ₀, a ₁..., a _m), converse M+2 point with above M+1 put relation.

Described conversion method utilizes the correlation of well data and the character of Taylor series, simulates the value of M+2 point according to the coefficient of trying to achieve, exponent number

4) according to step 3) estimated value that calculates, obtain the error of M+2 point.

The calculating of described estimated value error, is the estimated value utilizing matching to obtain, subtracts each other with actual value, obtain error.

5) according to step 3) any relational expression after the known point that obtains and known point, step by step calculation goes out the estimated value of follow-up data, and the error of estimated value and actual value.

6) transmitting data is steps 2) in the known point of M+1 and step 5) error amount from M+2 point that calculates.

7) carrying out decompression processing to the data received, is utilize step 1) to step 3) method calculate the estimated value of subsequent point according to a front M+1 known point, then be superimposed with error, namely Distortionless goes out initial data.

Beneficial effect

The inventive method is estimated to realize lossless date-compress by Taylor series expansion, have the advantages that time space complexity is low, be applicable to oil field data distance transmission system, verified by the real system in certain oil field, transmitting network data load can be reduced to 45%.

Accompanying drawing explanation

Fig. 1 is that the system of the inventive method realizes block diagram;

Fig. 2 utilizes the power function of different rank to carry out compression effectiveness figure to electrical quantity data.

Embodiment

Below in conjunction with drawings and Examples, the present invention will be further described.

Embodiment

1) discrete type well data actual acquisition arrived is by M rank Tavlor series expansion.

Described decomposed form is, well data function representation is: y _n=f (x _n) | x _n=nT _s, n ∈ [1, N], wherein, T _sit is the sampling period.Function decomposition is M rank: y _n=a _mx _n ^m+ ... + a ₁x _n+ a ₀+ e _n.

2) determine by step 1) in the variable of Tavlor series expansion: M (optimal factor of function); a _m~ a ₀(coefficient of function).

First according to the Cramer rule solving linear equation, determine that M+1 coefficient needs M+1 independently linear equation, introduce assumed condition: e _n=0, n ∈ [1, M+1].

Can by step 1) M rank breakdown be modified to: $\left\{\begin{array}{c}{y}_1=a_M{x}_1^M+...+a_1{x}_1+a_0\\ y_2=a_M{x}_2^M+...+a_1{x}_2+a_0\\ .\\ .\\ .\\ y_{M+1}=a_M{x}_{M+1}^M+...+a_1{x}_{M+1}+a_0\end{array}\right.,$

Be rewritten into matrix form: y=Xa.Again because x _n=nT _s, n ∈ [1, N], so X is generalized circular matrix, only has relation with matching exponent number M, after be denoted as X _m+1, meet full rank condition: r (X _m+1)=M+1, the inverse matrix of existence anduniquess and coefficient vector: a=X _m+1 ^-1y.Optimal factor M, determine according to minimal compression rate, concrete grammar is see step 6).

3) according to step 2) the coefficient a that obtains and front M+1 known point X, converse the relation with M+2 point, thus estimate the value of M+2 point, obtain the error of estimated value and actual value.

The estimated value of M+2 point: $\begin{array}{c}{\hat{y}}_{M+2}=a_M{x}_{M+2}^M+a_{M-1}{x}_{M+2}^{M-1}+...+a_0\\ =\left(\begin{array}{cccc}{x}_{M+2}^M& x_{M+2}^{M-1}& \mathrm{...}& x_{M+2}^0\end{array}\right)a\\ ={\mathrm{mX}}_{M+1}^{-1}y\end{array}$

And m and all only relevant with the value of matching exponent number M.Order be rewritten as the linear combination about y: $\begin{array}{c}{\hat{y}}_{M+2}=by\\ =b_{M+1}{y}_{M+1}+b_M{y}_M+\mathrm{...}+b_1{y}_1\end{array}$ Evaluated error is: $e_{M+2}=y_{M+2}-{\hat{y}}_{M+2}$

4) according to above-mentioned steps 1), step 2), step 3) method, regard the 1st as to M+1 bit data the 2nd to M+2 bit data, use same algorithm for estimating, progressively obtain the error amount that follow-up data is corresponding.

When after this estimating M+i point: obtain evaluated error sequence: E=(e _m+2, e _m+3..., e _n)

5) the transmission data after having processed are compressed.

The data sequence Y of initial M+1 point only need be transmitted in transmitting procedure _init=(y ₁, y ₂..., y _m+1) and evaluated error sequence { E}, namely data sequence waiting for transmission is: Y _trans=(y ₁, y ₂..., y _m, e _m+1..., e _n)

6) decompression processing is carried out to reception data, recover initial data.

Receiving terminal decompression procedure is the inverse process of compression, namely first can obtain out generalized circular matrix X by matching exponent number M _m+1, then utilize M+1 initiation sequence { Y _initaccording to step 1) and step 2) just can obtain Taylor Series coefficient vector a, then estimate next point data by these Taylor series finally by the error e received _m+2to estimated value revise, the actual value y of M+2 point can be obtained _m+2.Repeat above-mentioned decompression procedure, finally can realize data convert.

7) from transducer collects data, intercept one-period, utilize step 1) to step 4) method carry out compression and calculate, from M=1, obtain a temporary transient maximum estimated error magnitude: Δ E=max (E)-min (E).The Δ E on calculated for subsequent M+1 rank, circulates successively, when maximum estimated error magnitude becomes increase from the trend reduced, can jump out circulation, determine the matching exponent number of best Taylor series.Utilize the power function of different rank to compress electrical quantity data, the compression result obtained as shown in Figure 2.

Claims (1)

1., based on the well data lossless compression method that Taylor series are estimated, it is characterized in that, comprise following steps:

Step one: according to oil well actual acquired data situation, by calculating minimal compression rate, determines the optimal factor M of Tavlor series expansion.

Described compression ratio calculates, and is the data after being compressed by following steps and the data before compression, the bit wide ratio after quantification.

Step 2: to the data collected, by M rank Taylor series expansion, solves coefficient (a ₀, a ₁..., a _m).

The coefficient of described Tavlor series expansion is determined to utilize front M+1 known point, lists M+1 decomposition formula, obtains by separating math equation calculating.

Step 3: the M obtained according to step one and step 2 and (a ₀, a ₁..., a _m), converse M+2 point with above M+1 put relation.

Described conversion method utilizes the correlation of well data and the character of Taylor series, simulates the value of M+2 point according to the coefficient of trying to achieve, exponent number

Step 4: the estimated value calculated according to step 3, obtains the error of M+2 point.

The calculating of described estimated value error, is the estimated value utilizing matching to obtain, subtracts each other with actual value, obtain error.

Step 5: any relational expression after the known point obtained according to step 3 and known point, step by step calculation goes out the estimated value of follow-up data, and the error of estimated value and actual value.

Step 6: transmission data are error amounts from M+2 point that the known point of M+1 in step 2 and step 5 calculate.Carry out decompression processing to the data received, be the estimated value utilizing step one to the method for step 3 to calculate subsequent point according to a front M+1 known point, then be superimposed with error, namely Distortionless goes out initial data.