Determination of mineral rock composition is an important part of unconventional reservoir formation evaluation because the mineral composition affects hydraulic fracture generation and propagation. Two types of models are usually used for mineralogy modeling—deterministic and stochastic. Both models apply mathematical representations of the logging tool responses; however, stochastic modeling has become more popular due to its consideration of random distributions in the predictor and target variables. Stochastic mineralogy modeling algorithms usually produce solutions by minimizing a function reflecting the differences between the measured and modeled responses. However, due to the non-uniqueness inherent in inversion methods, the solution may not provide petrophysically meaningful results. To avoid producing compromised results, the use of geological constraints is proposed to represent the geological relations between the unknown parameters (inversion variables), leading to a more meaningful mineralogy model. The proposed algorithm incorporates probability functions to generate mineralogical solutions representing geologically and petrophysically sound results. The weight assigned to the penalties in the cost function depends on the probability function assigned to the constraints. Two models are presented using the proposed algorithm: a pyrite-anhydrite constraint based on the iron and sulfur ratio, and a K-feldspar-albite constraint based on the thorium and potassium ratio. Data sets from several different shale plays, from across North America, are processed using the proposed algorithm. The mineral sets are complex and vary from one play to another. The results show excellent agreement with the available core X-ray diffraction measurements. The study demonstrates that the proposed constraints provide an effective improvement, in integrated formation evaluation, especially in unconventional reservoirs with highly complex mineralogy.

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Hydrocarbons are bypassed in known fields. This is due to reservoir heterogeneities, complex lithology, and limitations of existing technology. This paper seeks to identify the scenarios of bypassed hydrocarbons, and to highlight how advances in reservoir characterization techniques have improved assessment of bypassed hydrocarbons. The present case study is an evaluation well drilled on the continental shelf, off the West African Coastline. The targeted thin-bedded reservoir sands are of Cenomanian age. Some technologies for assessing bypassed hydrocarbon include Gamma Ray Spectralog and Thin Bed Analysis. NMR is important for accurate reservoir characterization of thinly bedded reservoirs. The measured NMR porosity was 15pu, which is 42% of the actual porosity. Using the measured values gave a permeability of 5.3mD as against the actual permeability of 234mD. The novel model presented in this paper increased the porosity by 58% and the permeability by 4315%.

The acquisition of openhole logging data is not always guaranteed because of difficult drilling environments. In such cases, formation evaluation, and thus the completion program, becomes a real challenge. The situation becomes more complex when dealing with unconventional reservoirs with very tight carbonates and organic carbon-rich formations. This paper presents a method to measure the total organic carbon (TOC), which, in this paper, represents the organic carbon in the matrix (kerogen and coal), and to estimate oil saturation in such a challenging environment. A suite of wireline tools (gamma ray (GR), Spectralog, density, neutron, nuclear spectroscopy) was run through 7.625-in. casing to evaluate the formation and to quantify TOC and the oil in the pores. The nuclear spectroscopy tool, which was the master tool, measures the total carbon in the formation. Part of this carbon is attributed to the inorganic matrix (carbonates). Another part is attributed to the organic matter in the matrix (kerogen). The remaining carbon, or excess carbon, is mainly the carbon inside the pores. The process consists of integrating the conventional logging data, spectroscopy data, core data, and some geological constraints to estimate corrected porosity, mineralogy, and TOC in the kerogen-rich intervals. The excess carbon, which is not attributed to the matrix and TOC, is used to estimate oil saturation. Finally, core data are used to validate the analysis results. The presented methodology has been applied to a casedhole well with no openhole data previously acquired due to drilling issues. The primary target of the well, in the deep section, produced water; then, the operator decided to revisit the second target and complete it for testing. It has to be pointed out that over the well-cemented intervals, the results showed a very good matching of the corrected total porosity and the core total porosity. Relying on TOC and saturation analysis results, the operator selected the most promising intervals to be tested. Testing results have shown excellent matching between production results and oil saturation analysis results. TOC and oil saturation quantification using nuclear spectroscopy technology and core data results showed its success in both tight carbonates and organic carbon-rich reservoirs. This method will be a solution to evaluate and complete any wells with no openhole data acquired, and also to evaluate and complete unconventional formations where the conventional methods have shown their limitations.

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