Hydraulic fracturing is currently the completion method of choice in most tight reservoirs; however, the ultimate performance of fractured wells is severely affected by the interfering effects inside the fracture and interfractures. Previous simulation studies investigated the effects of well spacing and fracture length on well productivity in low-permeability oil and gas reservoirs. It was shown that the most important parameters for determining the optimum fracture length are the formation permeability and the stimulated reservoir volume (SRV). Although a number of studies have examined the performance of horizontal fractured wells and the fracture geometry effect, fracture spacing and intersecting angles in vertical and horizontal wells should be further investigated. This study presents the results of a tight oil reservoir analogy. Reservoir parameters considered include local rock stresses, rock compressibility, absolute and relative permeability, and porosity. The well-completion parameters included fracture length, height, width, conductivity, number and spacing between fractures, fracture intersecting angle, and cased- vs. openhole completion. Fracture modeling considered rigorous description of the hydraulic fracture properties and finite difference reservoir modeling. Economically attractive reserves recovery was modeled through multiple fracture placements in a 10,000-ft horizontal well. Numerical simulation showed that oil recovery increased between 8 to 15%, while net present value (NPV) increased 8 to 24%, as the number of fractures increased. Based on the critical assumptions in the study (permeability, natural fracture distribution, and stress orientation), an optimum number of fractures was identified. Openhole completions provided better performance in most cases, and recovery was greater for a higher number of contributing perpendicular vs. longitudinal fractures. The results of the study hopefully can be used to improve the understanding of the role of fracture geometry, spacing, and open/cased-hole completion strategy to enhance an operator's optimum completion design.

The carbonate gas producing zones of the Ghawar field have been impacted by extensive FeS scale deposition, reducing overall gas production and significantly increasing risks of well interventions. Previous remediation included the use of workover rigs, which can be costly because of the time necessary for workovers and lost production. H 2 S levels (2 to 5%) found in the reservoir also contribute to higher costs and risks when using workover rigs.A chemical solution was also considered, but the FeS could not be 100% dissolved with HCl and the chemical reaction generated large amounts of H 2 S in addition to existing high levels of H 2 S in the reservoir. This poses a safety concern with the returns at surface along with potential corrosion of the coiled tubing (CT) and completion. Therefore, the safest and most economical method was deemed to be mechanical descaling with CT.This paper discusses two wells where mechanical descaling was applied using CT. Each well involved four major challenges that included low reservoir pressure, increased reservoir temperature, horizontal openhole completion, and scale with high specific gravity (3.7 to 4.3). The low reservoir pressure required pay zone isolation to allow for returns to circulate out the heavy scale and to minimize fluid losses to the formation. The fact that the wells had long, openhole sections created another challenge for isolation and cleanout. With a bottomhole temperature (BHT) as high as to 310°F, the operational envelope of temporary chemical packers in combination with loss circulation materials (LCMs) to isolate the openhole section had to be expanded. Following mechanical descaling with CT, the final challenge discussed in this paper is the process to clean out the LCM in the horizontal openhole and bring the well back to maximum gas production using pinpoint stimulation techniques.

Hydraulic fracture design and performance optimization has been studied extensively. Lean manufacturing strategies for the intensive fracturing operations requires completion optimization by careful understanding of marginal benefits that additional fracture stages will bring to the bottom line.This study presents the results of a tight oil reservoir analogy of the Bakken shale using a fully coupled reservoir and surface-network multiphase simulation orchestrated by an automated workflow to run multiple technical scenarios to understand the incremental economic value of adding additional fractures to the standard 20-fracture well design. Our effort included the use of assisted generation of grid refinements to estimate oil recovery in a multiple-fracture horizontal well.This study demonstrates that an increasing number of fractures will not always improve the short-term economics. Comparing 30-vs 60-fracture cases to low-permeability cases (k=0.005 and 0.05 md) indicates that NPV can be increased by USD 0.8 to 2.2 million-97 to 110%), whereas return on capital invested (ROCE) was improved by 3-4%. The methodology and the results of this study can confidently be used to advance the understanding of the role of fracture geometry and spacing to enhance an operator's optimum completion design.

This paper describes a case study combining more frequently used tools in the petroleum industry, such as volumetric analysis with Monte Carlo Simulation and Material Balance, to improve performance predictions in the carbonate mature fields offshore. Quantifying the uncertainties in original oil in place (OOIP) estimates can support development and investment decisions for individual reservoirs. In the early life of a reservoir, the well data is largely uncertain. Probabilistic estimates are commonly generated prior to significant production from a carbonate mature field by combining volumetric analysis with the Monte Carlo method. The Monte Carlo method was used to compute the oil in place using static reservoir properties, such as petrophysical parameters, which always involve a magnitude of uncertainty and, as such, should be treated as random variables with distinct probability distributions. To assess the profitability of the development project, it was necessary to use material balance for the field. Material balance evaluation has been identified as a useful tool for initially establishing connected hydrocarbon volume in place and for identifying reservoir drive mechanisms. This tool is often considered more accurate than volumetric methods, since the volumetric methods are based on dynamic reservoir data such as pressure and production, and thus can be applied only after the reservoir has produced for a significant period of time. The outcome of the Monte Carlo simulation was a range of reserve values with their associated probabilities of P10, P50, and P90. A commercial material balance software was used to carry out a combination of the analytical and graphical methods establishing the correct material balance model, thereby adding confidence in the obtained results for reserves in the field. OOIP was found to be approximately 235 to 245 MMSTB, of which ~21% is stored in the matrix system. During the execution of the project, the combination of methods can reduce the non-uniqueness of the material balance solution. Material balance can reduce the uncertainty in the range estimates, since they are based on observed performance data. Base case prediction forecasts only recovered 19% of OOIP, or an additional 9% from current recovery. This is in agreement with the references of similar fields. Based on 40-year prediction forecasts, an additional ~72.6 to 98 MMSTB of oil-equivalent reserves can be generated with an additional capital expenditure (CAPEX) of 450 to 600 MM$ for water or gas injection facilities and 10 to 14 wells, leading to a recovery factor of ~40.9 to 46.6 of OOIP. Introduction Field development planning is traditionally a sequential process; decisions are often segmented and disconnected. Typically, reservoir engineers model reservoir response to the bottom hole, production engineers model the whole wellbore to the well head, and process engineers model the surface facilities from the well head to the tank (Saputelli et al. 2002). For the above reasons, project results often deviate from the project plan. In the building of field development plans, in-situ hydrocarbon reserves represent a key uncertainty to be analyzed. Because of poor predictability in hydrocarbon reserves, surface facilities may remain sub-utilized, a reservoir's full potential may not be obtained, and field economics may not reach peak performance. Field development decisions must be made despite uncertainties in hydrocarbon reserves. The heterogeneity of information and the complexity of current hydrocarbon assets require an iterative approach to identify the best opportunities.

Productivity index (PI) reduction is a recognized phenomenon in oil and gas production to the extent that production becomes uneconomic. Productivity index (PI) decrease may be caused by several factors arising from reservoir, completion, and operational issues. The reservoir-related factors include compaction, fines migration, pressure support and multi-phase fluid flow. The completion-related issues arise from frac-pack geometry and stress-sensitivity of proppant conductivity. Finally, operational issues such as pressure drawdown at the sandface and induced flux (the movement of fluids across the completion) may also play a large role in PI degradation. Understanding the interactions of different parameters controlling the PI behavior and the resultant well performance is paramount for maximizing the ultimate recovery and net-present value or NPV. PI modeling may offer several challenges because it involves non-unique solutions for reservoirs and near-wellbore and well-completion status, in addition to manual data entry subject to human errors, uncertainties in reservoir parameters, and poor quality field data. This study presents a workflow for modeling well PI degradation for an over-pressured gas/condensate reservoir in deep offshore Gulf of Mexico. An automated procedure was tailored and applied to match production history by adjusting both the reservoir and well completion parameters. Among the reservoir parameters considered were horizontal stresses, rock compressibility, absolute permeability, relative-permeability endpoints and curvature, porosity, stress-sensitive permeability and porosity. The well completion parameters included fracture length, height, width, conductivity, and stress. A "Tabu Scatter Search" optimizer engine (April et al, 2003) selected the optimal values of a set of input parameters to minimize the objective function, i.e. the error between the measured and calculated PI values. Both reservoir and well completion parameters contributing to PI degradation were identified, and compared to those obtained by alternate methods. The relative and combined contributions of stress-sensitive reservoir compressibility, permeability, porosity, and fracture conductivity all helped understand well performance and guide decisions towards optimum well operating envelope.