基于岩性识别方法的岩层智能识别方法是基于机器学习分类算法，可以对测井数据和测井图进行分类和识别，有效地学习和记忆储层中岩层的特征。\n
基于岩性识别方法的岩层智能识别方法是基于机器学习分类算法，可以对测井数该方法可以比较吸附岩体、决策树、随机森林和SVM四种算法，选择出最高的岩性识别精度。为满足识别精度、效率等要求提供重要依据，对测井数据的自动解释和计算机的地层自识别具有重要意义。\n
TZA井位于塔里木盆地。其所在位置为泥盆纪东河塘组，含细砂岩等三种岩性。", "ds_source": "
实验测得。", "ds_process_way": "
为了探讨不同算法对精度的影响，采用50%的训练比和8个测井数据对TZ4井进行了测试。在总数据集上的训练数据量对训练精度的影响较大。采用五组场景来调查不同的训练比例对预测结果的影响。采用决策树算法和8个日志数据来改变训练数据的比例。", "ds_quality": "
每个案例都被测试了10次，并取平均值以减少误差。根据不同训练集体积的典型结果发现只训练总数据集的30%，准确率可以达到80%以上。此外为探讨不同测井参数对精度的影响，采用决策树算法和50%的训练比例进行了多组场景分析。\n
通过添加测井参数的数量来测试参数对模型的影响，可以得到通过测井参数的增加，模型的准确率之间提高，最终准确率可以达到90%以上。", "ds_acq_start_time": "2009-01-01 00:00:00", "ds_acq_end_time": "2013-12-31 00:00:00", "ds_acq_place": "塔里木盆地", "ds_acq_lon_east": 90.0, "ds_acq_lat_south": 37.0, "ds_acq_lon_west": 75.0, "ds_acq_lat_north": 42.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 3754615, "ds_files_count": 2, "ds_format": "xlsx", "ds_space_res": "0.1-10m", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "0190eb3c-7c8c-4bc5-a1b6-de0ab83379c4.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "fdaf40e0-8913-40fa-927b-8786c3af0bf5", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.50" ], "quality_level": 3, "publish_time": "2024-03-28 09:26:06", "last_updated": "2025-06-30 11:30:27", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.XDA14.DB6395.2024", "i18n": { "en": { "title": "Well logging data set of TZ4 wells in the Devonian Donghetang Formation, Tarim Basin (2009-2013)", "ds_format": "xlsx", "ds_source": "
Experimentally measured.", "ds_quality": "
Each case was tested 10 times and averaged to minimize error. Typical results based on different training set volumes found that by training only 30% of the total dataset, the accuracy can reach more than 80%. In addition to explore the effect of different logging parameters on accuracy, multiple sets of scenarios were analyzed using a decision tree algorithm and a 50% training ratio.\n
By adding the number of logging parameters to test the effect of parameters on the model, it can be obtained that through the increase of logging parameters, the accuracy of the model is improved between, and the final accuracy can reach more than 90%.", "ds_ref_way": "", "ds_abstract": "
Intelligent recognition method of rock formation based on lithology recognition method is based on machine learning classification algorithm, which can classify and recognize logging data and logging map, and effectively learn and memorize the characteristics of rock formations in the reservoir.\n
The rock formation intelligent recognition method based on lithology recognition method is based on machine learning classification algorithm, which can classify and recognize the logging data and logging charts The method can compare four algorithms, namely, adsorbed rock body, decision tree, random forest, and SVM, and select the highest lithology recognition accuracy. To meet the recognition accuracy, efficiency and other requirements to provide an important basis for the automatic interpretation of logging data and computerized stratigraphic self-recognition is of great significance.\n
The TZA well is located in the Tarim Basin. Its location is Devonian Donghetang Formation, which contains three lithologies including fine sandstone.
", "ds_time_res": "", "ds_acq_place": "Tarim Basin", "ds_space_res": "0.1-10m", "ds_projection": "", "ds_process_way": "In order to explore the effect of different algorithms on the accuracy, the TZ4 wells were tested using a 50% training ratio and eight logs. The amount of training data on the total dataset has a large impact on the training accuracy. Five sets of scenarios were used to investigate the effect of different training ratios on the prediction results. A decision tree algorithm and eight log data were used to vary the ratio of training data.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "泥盆纪东河塘组", "测井曲线", "油气测井", "塔里木盆地", "岩性识别" ], "ds_subject_tags": [ "地质学" ], "ds_class_tags": [], "ds_locus_tags": [ "塔里木盆地" ], "ds_time_tags": [ 2009, 2010, 2011, 2012, 2013 ], "ds_contributors": [ { "true_name": "陈冬", "email": "dong.chen@cup.edu.cn", "work_for": "中国石油大学（北京）石油工程学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈冬", "email": "dong.chen@cup.edu.cn", "work_for": "中国石油大学（北京）石油工程学院", "country": "中国" } ], "ds_managers": [ { "true_name": "陈冬", "email": "dong.chen@cup.edu.cn", "work_for": "中国石油大学（北京）石油工程学院", "country": "中国" } ], "category": "其他" }