A computer-implemented method for controlling oilfield equipment comprising: representing oilfield well plan information as a two-dimensional image comprising pixels in a two-dimensional array of values, wherein the first dimension of the array represents time points in the well plan and the second dimension represents state variables associated with the well plan , wherein the well plan information includes specification of activities involved in performing actions in the well plan, times associated with performing the activities and resources for performing the activities and wherein the state variables include operational states of equipment, pressure data, rock condition data, fluid flow data and trajectory data that define a drilling process for a wellsite ; training a convolutional deep neural network based on the pixels to generate a trained deep neural network , wherein the training comprises a supervised learning phase where spatially correlated features in the image representing states of the well drilling plan are identified to generate a preliminary version of a value network, and a deep reinforcement learning phase where control actions associated with a well drilling plan are evaluated against the value network to learn a policy network establishing a mapping between the states and actions; implementing the trained convolutional deep neural network during generation of an oilfield well plan , wherein the trained deep neural network is utilized to analyze the pixel-based image to determine a current state of the well plan and to determine a next state, where the next state is a desirable state that can be achieved via one or more control actions associated with the well plan; and outputting the oilfield well plan as a digital plan that specifies the determined control actions for the oilfield equipment . representing oilfield well plan information as a two-dimensional image comprising pixels in a two-dimensional array of values, wherein the first dimension of the array represents time points in the well plan and the second dimension represents state variables associated with the well plan , wherein the well plan information includes specification of activities involved in performing actions in the well plan, times associated with performing the activities and resources for performing the activities and wherein the state variables include operational states of equipment, pressure data, rock condition data, fluid flow data and trajectory data that define a drilling process for a wellsite ; training a convolutional deep neural network based on the pixels to generate a trained deep neural network , wherein the training comprises a supervised learning phase where spatially correlated features in the image representing states of the well drilling plan are identified to generate a preliminary version of a value network, and a deep reinforcement learning phase where control actions associated with a well drilling plan are evaluated against the value network to learn a policy network establishing a mapping between the states and actions; implementing the trained convolutional deep neural network during generation of an oilfield well plan , wherein the trained deep neural network is utilized to analyze the pixel-based image to determine a current state of the well plan and to determine a next state, where the next state is a desirable state that can be achieved via one or more control actions associated with the well plan; and outputting the oilfield well plan as a digital plan that specifies the determined control actions for the oilfield equipment . <EOS>