This paper shows a systematic approach to reduce uncertainty of volume, recovery factors and production forecasts with a closed loop of static and dynamic information over the life of the reservoirs. A case study of a Middle Marrat reservoir block in the North Kuwait Jurassic Complex (NKJC) is presented to illustrate the procedures. The NKJC comprises of six discovered fields each consisting of at least four prospective producing units containing multiple fluid types at near critical conditions. Intra-field compartmentalization due to fracture intensity, faulting and litho-facies variations resulting in porosity / permeability pinch outs has further created isolated blocks with a high level of uncertainty in the reservoir parameters describing the storage and flow characteristics necessary for forecasting well behaviors resulting from connected volumes. It is vital to collect as much dynamic data as possible at an early stage of the development for reserve booking and production forecast. Field development plan envisaged for the NKJC considered a three-phase development in order to mitigate CAPEX risk, reduce uncertainty and optimize recovery. Each phase has sufficient gap (3–4 years) to drill wells, collect formation evaluation and well performance data and adjust future plans as necessary. This paper describes the building blocks of volumetric estimates and fluid flow characteristics, considering the uncertainty levels in litho-facies spreads, porosity / permeability relationships, water saturations and fracture geometries etc. based on initial drilling and limited well tests. After the start of production, fluid properties monitoring, collection of well surveillance data such as pressure vs. rate curves, pressure transient analysis (PTA), production logging surveys (PLT), Modular Formation Dynamics Tester (MDT) and material balance calculations indicate another set of storage and flow properties. Integrating these estimates with geology, structural framework and fracture geometry is the key to obtaining a consistent reservoir description. Consistency in turn, reduces uncertainty in the connected volumes and flow dynamics over time. Flow assurance calculations using simulation model calibrated with the integrated set of parameters have been made with confidence to support a lifecycle reservoir management strategy to optimize recovery.
Case study illustrates that Green Field development plans benefit greatly from early dynamic data collection integrated with volumetric estimations by reducing uncertainty of reservoir description parameters that are used to calculate in-place hydrocarbon volumes and reserves. Reservoir uncertainty in naturally fractured carbonates is maximum with consequent risks. The methodology and work flow thus generated may be repeated for other blocks in the complex and for similar naturally fractured reservoirs.
Introduction
In any oil company involved in Exploration and Production (E&P) activity, an exploration success creates an atmosphere of optimism and expectations that is hard to contain. Professional technical staff of these companies is faced with the challenge of assessing the true significance of a discovery, accurately estimating the discovered in-place volumes of hydrocarbons and racing against time to develop and convert these reserves into financial success to maximize net present value of the discovered assets. Unfortunately, uncertainty of subsurface reservoir descriptive parameters is highest in the early field life, leading to a high risk of either under- or overestimating the in-place volumes and estimates of ultimate recoveries. Implications of wrongly guessing on either side and inefficiently committing capital and human resources are an economic loss for the project. Early development risk mitigation involves computing volumes with large uncertainty of input parameters, heavy use of analogues to fill information gaps and multiple scenario analyses to arrive at a range of computed outcomes. Based on the risk vs. cost of failure assessments, companies spend considerable time and resources to collect additional dynamic data and/or adapt a phased approach to allocation of resources to mitigate risk. Either way, oilfield development is a journey on a learning curve that concludes only with the last barrel of production from a reservoir.
