TECHNICAL FIELD

Aspects of the present disclosure relate to exploration, evaluation, development, and production of a reservoir, and more particularly to systems and methods for identifying target regions of a reservoir having a high probability of production.

BACKGROUND

A reservoir is subsurface pool of a natural resource, such as oil and/or gas, contained within rock formations, which have varying levels of porosity and permeability. The porosity is dictated by a volume of the natural resource and the pore volume of the rock, while the permeability relates to the ability of the rock to allow the natural resource to flow through for collection. Reservoirs are identified using hydrocarbon exploration techniques that involve drilling along a well trajectory. Well logs are a concise, detailed plot of formation parameters versus depth that are captured using logging tools deployed along the well trajectory. Using the well logs, professionals may identify lithologies, differentiate between porous and nonporous rock, and identify payzones in the subsurface rock formations where the natural resource exists in exploitable quantities.

However, while characteristics of the petrophysical phenomena, including porosity and permeability, along the well trajectory may be known, uncertainty of the petrophysics of the subformation increases as distance away from the well trajectory increases. Accordingly, reservoir modeling is utilized to estimate the petraphysics for use in decision making regarding field development, future production prediction, well placement, and other reservoir production activities. Conventionally, a suite of professionals are involved in gathering the data, generating the model, and employing the model in decision making. Each of these professionals is typically utilizing a discrete tool that outputs results dictated by underlying assumptions by the professional and handing off the results to another professional to use in the next step. As such, the end to end process is conventionally plagued with human error and bias in the results generated by each tool with no retention of the disparate professional opinions that were presented during the discrete processes but rejected. In addition to human error and bias influencing the results, conventional systems and methods have inconsistent workflows and inefficient handoffs and fail to integrate the discrete tools with disparate programming languages and to retain alternative opinions, assumptions, and underlying data. Overall, conventional systems and methods fail to meaningfully reduce uncertainty and risk in reservoir exploration, evaluation, development, and production. It is with these observations in mind, among others, that various aspects of the present disclosure were conceived and developed.

SUMMARY

Implementations described and claimed herein address the foregoing problems by providing systems and methods for developing a reservoir. In one implementation, a reservoir model is received at a fuzzy inference engine. The reservoir model includes a static model and a dynamic model. The static model includes one or more clusters of a three-dimensional volume of the reservoir and an uncertainty quantification generated using a neural network. The dynamic model includes pressure values and fluid saturation values propagated across the three-dimensional volume through a nodal connectivity of neighboring clusters. A set of one or more input features is generated from the static model and the dynamic model using the fuzzy inference engine. The set of one or more input features is related to a drilling attractiveness of a target region of the reservoir using a set of one or more rules executed by a fuzzy inference engine. A quantification of the drilling attractiveness is generated. A recommendation for drilling in the reservoir is output based on the quantification of the drilling attractiveness.

Other implementations are also described and recited herein. Further, while multiple implementations are disclosed, still other implementations of the presently disclosed technology will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative implementations of the presently disclosed technology. As will be realized, the presently disclosed technology is capable of modifications in various aspects, all without departing from the spirit and scope of the presently disclosed technology. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not limiting.

