A system, method and computer program product for generating well location plans and field development plans assessing and ranking the potential of the different plans with a small number of parameters or initial conditions, thus considerably reducing the decision time for taking a particular strategy when compared with the techniques described in the art.

A method for extracting coalbed gas. A wellhead device delivers power fluid into a downhole power fluid pipe in a well shaft, and conveys the fluid to a pump in a pump cylinder connected with the downhole power fluid pipe. The pump sucks in formation fluid via a suction inlet, mixes the fluid with the power fluid to produce a mixed fluid, and conveys the mixed fluid to ground surface. The mixed fluid containing coal dust travels at a flow rate greater than a sedimentation rate of the coal dust, passes though the wellhead device, and flows to the ground surface, thereby preventing a sedimentation of the coal dust. The suction inlet of the pump reaches a lower boundary of a coalbed so as to prevent coal dust from burying the coalbed, and the coalbed gas automatically shoots through an annular space of a well shaft casing.

The present invention provides a fissured substrata water pumping apparatus and methods thereof. The fissured substrata water pumping apparatus includes a water pumping pipe inserted into a drilled hole under a roadway floor; one or more unidirectional water-blocking plates configured inside the water pumping pipe; a servo pump; and an annular drainage siphon having a first end connected to an upper end of the water pumping pipe, and a second end connected to an inlet end of the servo pump through a valve.

Systems and methods for completion design are disclosed. Wellsite data is acquired for one or more existing production wells. The wellsite data is transformed into model data sets for training a first machine learning (ML) model to predict well logs. A first well model uses the well logs to estimate production of the existing well(s). Parameters of the first well model are tuned based on a comparison between the estimated and actual production of the existing well(s). A second ML model is trained to predict parameters of a second well model for a new well, based on the tuned parameters of the first well model. The new well's production is forecasted using the second ML model. Completion costs for the new well are estimated based on the well's completion design parameters and the forecasted production. Completion design parameters are adjusted, based on the estimated completion costs and the forecasted production.

A high-integrity pressure protection system Christmas tree is provided. In one embodiment, an apparatus includes a Christmas tree, a choke coupled to receive fluid from the Christmas tree, and a high-integrity pressure protection system. The high-integrity pressure protection system includes pressure sensors downstream of the choke, valves upstream of the choke, and a logic solver connected to control operation of the valves of the high-integrity pressure protection system that are upstream of the choke. Further, the valves of the high-integrity pressure protection system that are upstream of the choke include at least two valves of the Christmas tree. Additional systems, devices, and methods are also disclosed.

The disclosure notably relates to a computer-implemented method for multiperiod optimization of oil and/or gas production. The method comprises providing a controlled dynamical system. The controlled dynamical system describes the evolution over time of a state of an oil and/or gas reservoir. The method further comprises providing a time-dependent admissible set of controls. The controls describe actions respecting constraints for controlling oil and/or gas flow and/or pressure. The method further comprises providing time-dependent observations of the content of the reservoir. The method further comprises optimizing, with respect to the state of the reservoir, the controls and the observations, an expected value over a given time span of an objective production function of the state, the controls and the observations. This constitutes an improved solution for oil and/or gas production.

A method, apparatus, and program product may build parent-child well pairs from data associated with one or more wells in a basin and use one or more parameters associated with such well pairs to train or use a machine learning model to predict a production impact of an infill well on one or more neighboring wells in the basin.

Four-dimensional fluid flow data is received that is associated with a time dimension and I, J, and K dimensions. The four-dimensional fluid flow data includes, for each of plural time steps, a fluid flow amount for the respective time step and for a respective I, J, K cell. Using the four-dimensional fluid flow data and for each of plural time steps, a four-dimensional geocellular model is determined having I, J, K and t dimensions and indicating, for each I, J, K, t cell, an amount of a fluid flowing through the I, J, K cell for a respective time step t. A three-dimensional time-independent model is determined for the I, J, K cell. A two-dimensional time-independent model is determined that includes a cumulative fluid flow amount for each I, J cell.

Systems and methods for machine learning (ML) assisted parameter matching are disclosed. Wellsite data is acquired for one or more existing production wells in a hydrocarbon producing field. The wellsite data is transformed into one or more model data sets for predictive modeling. A first ML model is trained to predict well logs for the existing production well(s), based on the model data set(s). A first well model is generated to estimate production of the existing production well(s) based on the predicted well logs. Parameters of the first well model are tuned based on a comparison between the estimated and an actual production of the existing production well(s). A second ML model is trained to predict parameters of a second well model for a new production well, based on the tuned parameters of the first well model. The new well’s production is forecasted using the second ML model.

A method for predicting well production is disclosed. The method includes obtaining a training data set for a machine learning (ML) model that generates predicted well production data based on observed data of interest, generating multiple sets of initial guesses of model parameters of the ML model, using an ML algorithm applied to the training data set to generate multiple individually trained ML models based the multiple sets of initial model parameters, comparing a validation data set and respective predicted well production data of the individually trained ML models to generate a ranking, selecting top-ranked individually trained ML models based on the ranking, using the data of interest as input to the top-ranked individually trained ML models to generate a set of individual predicted well production data, and generating a final predicted well production data based on the set of individual predicted well production data.