Methods and systems for determining a liquid level above a horizontal segment of a wellbore in a formation are disclosed. Local temperatures and pressures are determined for each of a plurality of zones along the wellbore segment. For each zone, a local inflow rate is determined for fluids entering the wellbore from the formation. Based on the local inflow rate, local temperature, and local pressure, a local reservoir pressure is determined, and a local liquid level is determined based on the local reservoir pressure and a pressure associated with an injector wellbore positioned above the horizontal segment.

Methods and systems for determining a liquid level in a formation between a horizontal segment of an injection wellbore and a horizontal segment of a production wellbore are disclosed. Under shut-in conditions, local temperatures and pressures are determined for each of a plurality of inflow zones along the production wellbore segment. Local profile values are determined based on local shut-in subcool values and local shut-in liquid levels. After flow has resumed, a local liquid level is determined based on the local operating subcool value and the local profile value for that inflow zone. The local profile values may be updated during subsequent shut-ins.

Systems and methods for intelligent estimation of productivity index and reservoir pressure values using pressure sensors, a neural network model comprising historical flow rate data of at least a well bore, and a data processor. The pressure sensors generate pressure data associated with a well bore's surface point and a downhole point. The data processor, communicatively coupled to the two pressure sensors and the neural network model, is operable to receive the pressure data from the sensors respectively indicative of pressure at each of the two points, estimate a real-time productivity index value in real-time based on the pressure data from the pressure sensors and the historical flowrate data of the neural network model, and estimate a reservoir pressure value of the well bore at a flowing condition, a reservoir pressure value of the well bore at a shut-in condition, or both, based on the real-time productivity index.

Systems and methods for intelligent estimation of productivity index and reservoir pressure values using pressure sensors, a neural network model comprising historical flow rate data of at least a well bore, and a data processor. The pressure sensors generate pressure data associated with a well bore's surface point and a downhole point. The data processor, communicatively coupled to the two pressure sensors and the neural network model, is operable to receive the pressure data from the sensors respectively indicative of pressure at each of the two points, estimate a real-time productivity index value in real-time based on the pressure data from the pressure sensors and the historical flowrate data of the neural network model, and estimate a reservoir pressure value of the well bore at a flowing condition, a reservoir pressure value of the well bore at a shut-in condition, or both, based on the real-time productivity index.

Embodiments for monitoring groundwater discharge by one or more processors are described. A groundwater head at each of at least some of a plurality of locations is measured. A groundwater discharge for at least one of the plurality of locations is determined based on the measured groundwater heads. The determined groundwater discharge is compared to a groundwater discharge threshold associated with the at least one of the plurality of locations.

Embodiments for monitoring groundwater discharge by one or more processors are described. A groundwater head at each of at least some of a plurality of locations is measured. A groundwater discharge for at least one of the plurality of locations is determined based on the measured groundwater heads. The determined groundwater discharge is compared to a groundwater discharge threshold associated with the at least one of the plurality of locations.

With an information processing apparatus 1 that includes: a calculation unit that acquires, based on values respectively applied to two or more parameters related to a state of circulation of water resources in a predetermined area, water balance calculation values regarding a balance of the water resources in the predetermined area; and an output unit that outputs information regarding the state of circulation of the water resources in the predetermined area, which is based on results of the acquisition of the water balance calculation values respectively corresponding to two or more parameter value groups in which values of at least one of the parameters are different from each other, it is possible to output information regarding the circulation of the water resources in the predetermined area.

With an information processing apparatus 1 that includes: a calculation unit that acquires, based on values respectively applied to two or more parameters related to a state of circulation of water resources in a predetermined area, water balance calculation values regarding a balance of the water resources in the predetermined area; and an output unit that outputs information regarding the state of circulation of the water resources in the predetermined area, which is based on results of the acquisition of the water balance calculation values respectively corresponding to two or more parameter value groups in which values of at least one of the parameters are different from each other, it is possible to output information regarding the circulation of the water resources in the predetermined area.

Systems and methods include a computer-implemented method for predicting locations of aquifers. Missing measurements in groundwater chemistry data from aquifers are replaced with calculated equivalents. The groundwater chemistry data is split into a model building/optimizing dataset and a model validation dataset. The groundwater chemistry data is normalized. A dimensionality reduction is performed on the groundwater chemistry data, including identifying principal components. An aquifer prediction model is generated and trained using the model building/optimizing dataset and machine learning. Performance of the aquifer prediction model is validated using the model validation dataset. Locations of aquifers are predicted during production of a new well using the aquifer prediction model and groundwater chemistry data for the new well. Settings, targets, and parameters of equipment used for drilling are changed based on the predicted locations of aquafers and the groundwater chemistry of the aquifers.

Systems and methods include a computer-implemented method for predicting locations of aquifers. Missing measurements in groundwater chemistry data from aquifers are replaced with calculated equivalents. The groundwater chemistry data is split into a model building/optimizing dataset and a model validation dataset. The groundwater chemistry data is normalized. A dimensionality reduction is performed on the groundwater chemistry data, including identifying principal components. An aquifer prediction model is generated and trained using the model building/optimizing dataset and machine learning. Performance of the aquifer prediction model is validated using the model validation dataset. Locations of aquifers are predicted during production of a new well using the aquifer prediction model and groundwater chemistry data for the new well. Settings, targets, and parameters of equipment used for drilling are changed based on the predicted locations of aquafers and the groundwater chemistry of the aquifers.