[0072]
[0073] In the first embodiment of the present invention, a method for identifying light oil layers and condensate gas layers in a formation is provided.
[0074] data collection
[0075] In the first embodiment, the basic data acquisition is achieved through gas logging. This embodiment includes step 1, namely obtaining test wells.
[0076] The “obtained test well” refers to data sampling based on existing test wells. Of course, the “obtained” also includes newly established test wells.
[0077] For the specific method of establishing the test well, the present invention is not particularly limited. The establishment of test wells can be based on existing mature theories. At the same time, with the help of various required instruments and equipment, dynamic testing of downhole oil and gas is used for basic data collection. The basic principles or specific means on which data collection is based are not particularly limited in the present invention, as long as they have a qualified accuracy.
[0078] Regarding the number of test wells, in the present invention, the number of test wells may be one or more, preferably five or more, more preferably seven or more, and most preferably nine or more. For the first embodiment, the establishment of multiple test wells can make the final prediction or interpretation result of this embodiment more accurate.
[0079] For the distribution of test wells, each test well can be distributed in the test area according to actual needs. Preferably, for areas that need to be intensively tested, each test well can be evenly distributed at a certain interval. For each test well, it can be arranged linearly or arranged in an array with a certain shape. There is no special limitation on the interval between each test well in the present invention, as long as it meets the requirements of safe operation. For example, the interval between test wells can be 5000 meters or more, preferably 1000 meters or more.
[0080] In this embodiment, the test well is established in a manner perpendicular to the ground surface. It should be noted that "vertical" here includes the allowable actual error, such as ±5° or less.
[0081] Measure the gas composition of different depth layers in the test well, and the gas composition includes C in each layer 1 , C 2 , C 3 , IC 4 And nC 4 , And optional iC 5 And nC 5 Percentage content, where C 1 Represents a hydrocarbon with carbon atom 1, C 2 Represents a hydrocarbon with 2 carbon atoms, C 3 Represents a hydrocarbon with 3 carbon atoms, iC 4 Represents a hydrocarbon with a heterogeneous structure with 4 carbon atoms, nC 4 Represents a hydrocarbon with a positive structure with 4 carbon atoms. Preferably, the hydrocarbon gas is an alkane gas. It should be noted that for the gas sampling at different depths in the above test well, generally speaking, the gas samples can contain C 1 , C 2 , C 3 , IC 4 And nC 4 Such components, but whether there is iC in actual sampling 5 And nC 5 The composition depends on the different address structure or actual sampling method.
[0082] For the test of gas samples in each formation, a gas chromatograph equipped with flame ionization and thermal conductivity detectors can be used; for most components, the detection limit is 10 -7 ~10 -5; The main component analysis accuracy is within ±5%.
[0083] In step 1, the measurement of the gas composition of the layers at different depths in the test well includes measuring the gas composition of the layers at different depths or the same depth in different test wells. For the depth of the test well, the depth in the different depths is the vertical depth to the ground, and the depth range is 2000m-4000m, preferably 2500m-3500m. As mentioned above, the vertical here is the vertical including the allowable error.
[0084]
[0085] Collect gas logging data based on the above methods, and use the following methods to process the gas composition data of different depth layers in the logging data.
[0086] In step 2, the measured value of the gas composition is normalized, and the relative content of each gas component is calculated. The normalization method includes the following calculation (with no iC 5 And nC 5 Time as an example):
[0087]
[0088]
[0089]
[0090]
[0091]
[0092] The specific function of normalization is to summarize the statistical distribution of a unified sample, which limits the data to be processed (through a certain algorithm) within a certain range. First of all, normalization is for the convenience of subsequent data processing, and the second is to ensure faster convergence when the program is running. In the present invention, in one embodiment, in the normalization method that can be listed, C 1 The content is set to 100% or 1, etc., so as to further calculate the other components relative to C 1 Relative content.
[0093] Then, according to the normalized data obtained in step 2, step 3 is performed, that is, a graph is drawn based on the relative content distribution data of each component of each layer after the normalization processing in step 2, and the graph is based on the relative content It is the ordinate, and different gas components are the abscissa.
[0094] In a preferred embodiment of the present invention, the graph in step 3 is a graph established based on the data after thinning the data.
[0095] Among the various data obtained by normalization processing, there are often many records and there are unavoidable duplication or errors. In order to facilitate the visualization of graphs and reduce the judgment bias caused by noise, certain rules must be adopted to minimize the number of data points while ensuring the shape of the vector curve remains unchanged. This process is called Thin out. After the data is thinned out, the amount is greatly reduced, and it is basically guaranteed to reflect the basic shape characteristics of the original graph or curve, which can save space and time for further processing.
[0096] The data thinning method can be processed according to actual needs. Commonly used data thinning methods can be the step size method, the Douglas-Peuker algorithm, and the offset limit method. These methods can be implemented relatively quickly through computer readable programs.
[0097] In the preferred embodiment of the present invention, the Douglas-Peuker algorithm can be used. Generally, a complete curve or a certain line segment is considered from an overall perspective. The basic idea is:
[0098] 1) Connect a straight line to the first and last points of the curve, find the distance between all points on the curve and the straight line, and find the maximum distance value dmax, compare dmax with the preset threshold D:
[0099] 2) If dmax
[0100] If dmax≥D, keep the coordinate point corresponding to dmax and divide the curve into two parts with this point as the boundary. Repeat this method for these two parts, that is, repeat steps 1) and 2) until all dmax are equal
[0101] Obviously, the thinning precision of this algorithm is related to the threshold. The larger the threshold, the greater the degree of simplification and the more points are reduced. On the contrary, the lower the degree of simplification, the more points are retained, and the more the shape tends to the original curve. The determination of the threshold can be determined according to actual accuracy requirements.
[0102] In addition, the dilution accuracy of the DP algorithm is significantly improved compared to other thinning methods. On the one hand, because the threshold is generally the maximum allowable error of the corresponding feature, on the other hand, because the algorithm can achieve between deletion and retention A better balance, that is, it can fully reduce the number of points, while keeping the feature points as much as possible.
[0103] The graph produced after data dilution can more concisely and more intuitively reflect the change relationship of the gas distribution in each test layer, which is beneficial to the subsequent interpretation.
[0104] In step 3, in a preferred embodiment of the present invention, the ordinate is a logarithmic ordinate, and the gas component in the abscissa includes C 1 , C 2 , C 3 , IC 4 And nC 4 And optional iC 5 And nC 5 Represents the gas composition.
[0105] Specifically, in the process of gas logging and logging, the distribution of the content of different gases in layers of different depths is quite different. At the same time, the percentage content of different gases in the layers at the same depth or the relative content after normalization are also quite different. Therefore, when the ordinate of the general average linear scale is used, during the mapping process, limited by the size of the visible area, there will be too dense data distribution in some areas, and the data in some areas are too distributed and sparse, which is not conducive to observation And subsequent judgments. Therefore, in the present invention, preferably, the ordinate is set to a logarithmic coordinate. As the abscissa, C can be expressed at the same interval 1 , C 2 , C 3 , IC 4 , NC 4 , IC 5 And nC 5 Gas component. That is, in some preferred embodiments of the present invention, the graph according to step 3 is a graph in a semi-logarithmic coordinate system, and the "relative content" represented by the ordinate is a normalized relative number or relative amount, For example, take C 1 The content is set to 100 or 100% or 1, etc., and the calculated relative number of other components relative to C1.
[0106] According to the above set resume curve graph, the gas hydrocarbon component distribution curve in the measured or required different depth layers can be generated in one graph.
[0107]
[0108] In step 4, establish a gas line curve in the graph obtained in step 3, and the gas line curve is the (oil/gas) criticality of the distribution characteristics of the gas and hydrocarbon components of the oil and gas layer in the test well area. Trendline. Specifically, the hydrocarbon component of oil and gas formation gas is mainly composed of C 1 ~C 5 Mainly, namely C 1 , C 2 , C 3 , IC 4 , NC 4 , IC 5 And nC 5 Mainly. Specifically, in a certain layer, C 1 The content of (for example: methane) generally occupies more than 80%, the content of recombination is small, the distribution curve is steep, and the distribution of recombination is gradually reduced. Generally speaking, C 1 ~C 5 The content size distribution is: C 1 C 2 C 3 iC 4 ≥nC 4 iC 5 nC 5. The hydrocarbon in the present invention is preferably an alkane.
[0109] Specifically, in this embodiment, in step 4, the gas line curve may be a gas line curve established based on existing gas survey data in the area. The existing gas measurement data may be, for example, accurate gas measurement data obtained from gas measurement data of proven oil and gas layers in each layer of test wells in test wells in other test wells (other test locations) in the operating area; or Accurate gas measurement data obtained from actual measurement. Based on the existing gas survey data, the critical trend line of the gas hydrocarbon component distribution characteristics of the oil and gas reservoir, namely the gas line curve, is further studied.
[0110]
[0111] The method for identifying light oil layers and condensate gas layers in a formation provided by the present invention is based on the comparison between the relative content distribution data curve and gas line curve of the gas components of each layer, and the light oil layer or condensate gas layer in the formation Identify the gas evolution zone.
[0112] In this field, the humidity ratio (Wh), balance ratio (Bh) and characteristic ratio (Ch) are calculated through a large number of statistics of gas components of condensate gas layers and light oil layers, and light oil layers with different gas-oil ratios in test wells, There are differences in the well flow composition of condensate gas layers, but for example, humidity ratio (Wh) and equilibrium ratio (Bh) may sometimes be difficult to identify oil and gas layers, while characteristic ratio (Ch) can sometimes well identify oil and gas layers Wait. The method of the present invention can effectively identify light oil layers and condensate gas layers in the formation.
