A computer-implemented method of modelling an oil and gas network, the network comprising multiple branched flow paths, such as in multi-zonal wells and/or networks including multiple wells, and multiple control points at different branches, wherein each control point is a control device capable of applying a controlled adjustment to the oil and gas network to cause a variation in one or more flow parameter(s) in one or more flow path(s) of the network, and wherein modelling the network includes modelling of the variation of the one or more flow parameter(s) in one or more flow path(s) of the network; the method comprising: generating a long-term model using a first set of data relating to measurements of the one or more flow parameter(s) including pressure and/or temperature and the status of the control points over a first period of time, wherein the long-term model describes the relationship between flow rates, the status of control points, and the measured one or more flow parameter(s) including pressure and/or temperature; generating a short-term model using a second set of data relating to measurements of the one or more flow parameter(s) including pressure and/or temperature and the status of the control points over at least one second period of time, wherein the at least one second period of time is shorter than the first period of time, and wherein the short-term model describes the relationship between the status of control points and the one or more flow parameter(s) including pressure and/or temperature; and combining the short-term model with the long-term model by: using the short-term model to determine pressure and/or temperature values that will result from the status of one or more control points or from proposed changes to those control points; using the determined pressure and/or temperature values from the short-term model along with the status of, or the proposed changes to, the control points as inputs to the long-term model and then using the long-term model to determine flow rate values that will result from those inputs; and thereby obtaining a combined model allowing for estimation of flow rates in real time as well as prediction of the effects of changes in the status of one or more of the control points. generating a long-term model using a first set of data relating to measurements of the one or more flow parameter(s) including pressure and/or temperature and the status of the control points over a first period of time, wherein the long-term model describes the relationship between flow rates, the status of control points, and the measured one or more flow parameter(s) including pressure and/or temperature; generating a short-term model using a second set of data relating to measurements of the one or more flow parameter(s) including pressure and/or temperature and the status of the control points over at least one second period of time, wherein the at least one second period of time is shorter than the first period of time, and wherein the short-term model describes the relationship between the status of control points and the one or more flow parameter(s) including pressure and/or temperature; and combining the short-term model with the long-term model by: using the short-term model to determine pressure and/or temperature values that will result from the status of one or more control points or from proposed changes to those control points; using the determined pressure and/or temperature values from the short-term model along with the status of, or the proposed changes to, the control points as inputs to the long-term model and then using the long-term model to determine flow rate values that will result from those inputs; and thereby obtaining a combined model allowing for estimation of flow rates in real time as well as prediction of the effects of changes in the status of one or more of the control points. <EOS>