A precise assessment of tight gas operational efficiency is critical for investment decisions in unconventional reservoir development. However, quantifying production efficiency remains challenging due to the complex relationships between geological and operational factors. This study proposes a novel data-driven framework for predicting tight gas productivity, effectively integrating computing algorithms, machine learning algorithms, feature selection, production prediction and fracturing parameter optimization. A dataset of 3146 horizontal wells from the Montney tight gas field was used to train six machine learning models, aiming to identify the most significant factors. Results indicate that fluid-injection volumes, burial depth, number of stages, Young’s modulus, formation pressure, saturation, sandstone thickness and total organic carbon are the key variables for tight gas production. The Random Forest-based model achieved the highest accuracy of 88.6%. Case studies for the test demonstrate well that gas production could be nearly doubled by increasing fracturing fluid injection by 97.5%. This work provides evidence-based recommendations to refine development strategies and maximize reservoir performance.

The western Sulige gas field is a new and key reserve area for the rolling development of the Sulige gas field in China. However, due to the complex gas–water relationship and the difficulty in identifying gas and water formation, the scale and benefit deployment of the gas field are seriously restricted. In particular, almost all of the wells in the area produce water, and no water measurements have been carried out for any single well, which leads to an unclear understanding of the dynamic characteristics of the production wells, thus affecting the productivity calculations of the gas wells and the overall regional productivity evaluation. Based on the testing data for a gas well, the impacts of the reservoir property parameters on the gas and water production were analyzed by combining the production performance and static geological characteristics. It was determined that the physical parameters K, Kh, and φS_g had good positive correlations with gas production but not with water production; thus, effective prediction cannot be obtained for water production in gas well testing. After the analysis of the liquid-loading law, the gas wells were classified into three types: continuous liquid-carrying production, slight liquid-loading, and liquid-loading wells. In general, up to 96% of the gas wells were liquid-loaded. According to the different production performances exhibited in the different stages of the gas wells, five types of methods for diagnosing water production wells were proposed (gas testing, pilot production, gas–liquid two-phase measurement testing, liquid level detection, and production performance analysis), as well as the diagnostic criteria and corresponding solutions. To obtain real-time water production data for each well and investigate the change in the water–gas ratio (WGR) during the whole production process, a water production splitting method for gas wells based on three-dimensional geological modeling and numerical simulation combined with the constraints of the total water production of gas gathering stations was explored and established. The splitting results can be used to evaluate the water and gas productivity of gas wells and determine the best deliquification period. The gas well productivity when water production was considered was about 10% lower than that when water production was not considered. The best deliquification period was determined to be 125 days for wells with small water production, 20 days for wells with moderate water production, and 3 days for wells with serious water production. The results of this study could provide technical support for the scientific evaluation of gas well production indicators, reduction in development costs, and improvement in oil recovery.

In view of the characteristics of low permeability, strong heterogeneity, small effective sand scale and poor connectivity of tight gas reservoir, well pattern infilling optimization is the main method to improve gas recovery. Taking Sulige gas field as an example, based on the study of sedimentary facies, effective sand-body distribution, combined with production performance and engineering parameters, the well control drainage was obtained. Meanwhile, the interwell connectivity and interference probability was qualitatively analyzed by interference well test, and the critical well spacing density was determined. In order to quantitatively reveal the gas grabbing degree at different well spacing density and determine whether the final cumulative gas production of gas wells with interference is economical and feasible, the evaluation index of "gas well production interference ratio (GWPIR)" was defined specifically, and the intersection plot of GWPIR relating to gas reserve abundance and well spacing density was draw. Different from the traditional method of well number interference probability, through the combination of GWPIR plot and economic evaluation, well pattern optimization from both two perspectives of pursuing higher economic benefits and recovery degree can be realized, providing technical support for improving gas recovery and long-term stable production of gas field.

During the development of multi-layer tight sandstone gas reservoirs in Ordos Basin, China, it has not been easy to calculate accurately the production of each individual layer in gas wells. However, production allocation provides a vital basis for evaluating dynamic reserves and drainage areas of gas wells and remaining gas distributions of gas layers. To improve the accuracy and reliability of production allocation of gas wells, a new model was constructed based on the seepage equation, material balance equation, and pipe string pressure equation. In particular, this new model introduced the seepage equation with an elliptical boundary to accurately capture the fluid flow characteristics within a lenticular tight gas reservoir. The new model can accurately calculate the production and reservoir pressure of each individual layer in gas wells. In addition, the new model was validated and applied in the Sulige gas field, Ordos Basin. The following conclusions were drawn: First, The gas production contribution rates of pay zones based on the new model are fairly close to the measurements of the production profile logging, with errors less than 10%. Second, The overall drainage area of a gas well lies among those of each pay zone, and the total dynamic reserves of the well are close to the sum of the dynamic reserves of pay zones. Third, Higher permeability may lead to higher initial gas production of the pay zone, but the ultimate gas production contributions of pay zones are affected jointly by permeability and dynamic reserves. Finally, The new model has been successfully applied to the SZ block of the Sulige gas field, in which the fine evaluation of dynamic reserves, drainage areas, gas production, recovery factors, and remaining gas distributions of different layers was delivered, and the application results provide technical support for the future well placement and enhanced gas recovery of the block.

The western Sulige gas field is a new and key reserve area for the rolling development of the Sulige gas field in China. However, due to the complex gas–water relationship and the difficulty in identifying gas and water formation, the scale and benefit deployment of the gas field are seriously restricted. In particular, almost all of the wells in the area produce water and no water measurement has been carried out for any single well, which leads to an unclear understanding of the dynamic characteristics of the production wells, thus affecting the productivity calculations of the gas wells and the overall regional productivity evaluation. Based on the testing data for a gas well, the impacts of the reservoir property parameters on the gas and water production were analyzed by combining the production performance and static geological characteristics. It was determined that the physical parameters K, Kh, and φSg had good positive correlations with gas production but not with water production; thus, effective prediction cannot be obtained for water production in gas well testing. After the analysis of the liquid loading law, the gas wells were classified into three types: continuous liquid carrying production, slight liquid loading, and liquid loading wells. In general, up to 96% of the gas wells were liquid loaded. According to the different production performances exhibited in the different stages of the gas wells, five types of methods for diagnosing water production wells were proposed (gas testing, pilot production, gas–liquid two-phase measurement testing, liquid level detection, and production performance analysis), as well as the diagnostic criteria and corresponding solutions. To obtain real-time water production data for each well and investigate the change of water-gas ratio (WGR) during the whole production process, a water production splitting method for gas wells based on three-dimensional geological modeling and numerical simulation combined with the constraints of the total water production of gas gathering stations was explored and established. The splitting results can be used to evaluate the water and gas productivity of gas wells and determine the best deliquification period. The gas well productivity when water production was considered was about 10% lower than that when water production was not considered. The best deliquification period was determined to be 125 days for wells with small water production, 20 days for wells with moderate water production, and 3 days for wells with serious water production. The results of this study could provide technical support for the scientific evaluation of gas well production indicators, reduction of development costs, and improvement of oil recovery.